diff --git a/404.html b/404.html
index 58a6405d..3e5d12c0 100644
--- a/404.html
+++ b/404.html
@@ -24,7 +24,7 @@
 
     <a class="navbar-brand me-2" href="https://strengejacke.github.io/sjPlot/index.html">sjPlot</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.8.16.3</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.8.17</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/CODE_OF_CONDUCT.html b/CODE_OF_CONDUCT.html
index 06b620fa..81fbdfcf 100644
--- a/CODE_OF_CONDUCT.html
+++ b/CODE_OF_CONDUCT.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="index.html">sjPlot</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.8.16.3</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.8.17</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/CONTRIBUTING.html b/CONTRIBUTING.html
index 249965b6..ed16eae5 100644
--- a/CONTRIBUTING.html
+++ b/CONTRIBUTING.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="index.html">sjPlot</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.8.16.3</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.8.17</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/articles/blackwhitefigures.html b/articles/blackwhitefigures.html
index d9c4a9aa..e941ac33 100644
--- a/articles/blackwhitefigures.html
+++ b/articles/blackwhitefigures.html
@@ -23,7 +23,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">sjPlot</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.8.16.3</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.8.17</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/articles/custplot.html b/articles/custplot.html
index bdc70386..5c0021b6 100644
--- a/articles/custplot.html
+++ b/articles/custplot.html
@@ -23,7 +23,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">sjPlot</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.8.16.3</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.8.17</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/articles/index.html b/articles/index.html
index f92d611b..45458115 100644
--- a/articles/index.html
+++ b/articles/index.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">sjPlot</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.8.16.3</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.8.17</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/articles/plot_interactions.html b/articles/plot_interactions.html
index 7f638a1b..72433d7d 100644
--- a/articles/plot_interactions.html
+++ b/articles/plot_interactions.html
@@ -23,7 +23,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">sjPlot</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.8.16.3</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.8.17</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/articles/plot_likert_scales.html b/articles/plot_likert_scales.html
index f5433f4f..44360859 100644
--- a/articles/plot_likert_scales.html
+++ b/articles/plot_likert_scales.html
@@ -23,7 +23,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">sjPlot</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.8.16.3</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.8.17</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/articles/plot_marginal_effects.html b/articles/plot_marginal_effects.html
index 326a475b..397add60 100644
--- a/articles/plot_marginal_effects.html
+++ b/articles/plot_marginal_effects.html
@@ -23,7 +23,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">sjPlot</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.8.16.3</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.8.17</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/articles/plot_model_estimates.html b/articles/plot_model_estimates.html
index 9ba09d95..0ad815cb 100644
--- a/articles/plot_model_estimates.html
+++ b/articles/plot_model_estimates.html
@@ -23,7 +23,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">sjPlot</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.8.16.3</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.8.17</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/articles/sjtitemanalysis.html b/articles/sjtitemanalysis.html
index 5b5f0498..f5935a89 100644
--- a/articles/sjtitemanalysis.html
+++ b/articles/sjtitemanalysis.html
@@ -23,7 +23,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">sjPlot</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.8.16.3</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.8.17</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/articles/tab_bayes.html b/articles/tab_bayes.html
index 620998ff..8c1fc20c 100644
--- a/articles/tab_bayes.html
+++ b/articles/tab_bayes.html
@@ -23,7 +23,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">sjPlot</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.8.16.3</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.8.17</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/articles/tab_mixed.html b/articles/tab_mixed.html
index d0dfa625..73806d6d 100644
--- a/articles/tab_mixed.html
+++ b/articles/tab_mixed.html
@@ -23,7 +23,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">sjPlot</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.8.16.3</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.8.17</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/articles/tab_model_estimates.html b/articles/tab_model_estimates.html
index 91fc7bd6..dfbab2a1 100644
--- a/articles/tab_model_estimates.html
+++ b/articles/tab_model_estimates.html
@@ -23,7 +23,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">sjPlot</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.8.16.3</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.8.17</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/articles/tab_model_robust.html b/articles/tab_model_robust.html
index 01db5722..aad64848 100644
--- a/articles/tab_model_robust.html
+++ b/articles/tab_model_robust.html
@@ -23,7 +23,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">sjPlot</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.8.16.3</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.8.17</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/articles/table_css.html b/articles/table_css.html
index 7a2b9441..c4eb3f08 100644
--- a/articles/table_css.html
+++ b/articles/table_css.html
@@ -23,7 +23,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">sjPlot</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.8.16.3</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.8.17</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/authors.html b/authors.html
index f894f2ea..95ee83a4 100644
--- a/authors.html
+++ b/authors.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="index.html">sjPlot</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.8.16.3</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.8.17</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -89,12 +89,12 @@ <h2 id="citation">Citation</h2>
 
       <p>Lüdecke D (????).
 <em>sjPlot: Data Visualization for Statistics in Social Science</em>.
-R package version 2.8.16.3, <a href="https://CRAN.R-project.org/package=sjPlot" class="external-link">https://CRAN.R-project.org/package=sjPlot</a>.
+R package version 2.8.17, <a href="https://CRAN.R-project.org/package=sjPlot" class="external-link">https://CRAN.R-project.org/package=sjPlot</a>.
 </p>
       <pre>@Manual{,
   title = {sjPlot: Data Visualization for Statistics in Social Science},
   author = {Daniel Lüdecke},
-  note = {R package version 2.8.16.3},
+  note = {R package version 2.8.17},
   url = {https://CRAN.R-project.org/package=sjPlot},
 }</pre>
     </div>
diff --git a/index.html b/index.html
index 509aa1ce..c5f10eb8 100644
--- a/index.html
+++ b/index.html
@@ -26,7 +26,7 @@
 
     <a class="navbar-brand me-2" href="index.html">sjPlot</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.8.16.3</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.8.17</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/news/index.html b/news/index.html
index 7ecc4d49..c83abc1a 100644
--- a/news/index.html
+++ b/news/index.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">sjPlot</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.8.16.3</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.8.17</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -59,7 +59,8 @@
 <h2 class="pkg-version" data-toc-text="2.8.17" id="sjplot-2817">sjPlot 2.8.17<a class="anchor" aria-label="anchor" href="#sjplot-2817"></a></h2>
 <div class="section level3">
 <h3 id="bug-fixes-2-8-17">Bug fixes<a class="anchor" aria-label="anchor" href="#bug-fixes-2-8-17"></a></h3>
-<ul><li>Fixed issues with renamed arguments in upstream package <em>ggeffects</em>.</li>
+<ul><li><p>Fixed issues with renamed arguments in upstream package <em>ggeffects</em>.</p></li>
+<li><p>Several minor bug fixes.</p></li>
 </ul></div>
 </div>
     <div class="section level2">
diff --git a/pkgdown.yml b/pkgdown.yml
index 4d35a8cf..06ca0247 100644
--- a/pkgdown.yml
+++ b/pkgdown.yml
@@ -14,7 +14,7 @@ articles:
   tab_model_estimates: tab_model_estimates.html
   tab_model_robust: tab_model_robust.html
   table_css: table_css.html
-last_built: 2024-11-29T09:40Z
+last_built: 2024-11-29T10:51Z
 urls:
   reference: https://strengejacke.github.io/sjPlot/reference
   article: https://strengejacke.github.io/sjPlot/articles
diff --git a/reference/dist_chisq.html b/reference/dist_chisq.html
index efb7efae..e3005496 100644
--- a/reference/dist_chisq.html
+++ b/reference/dist_chisq.html
@@ -11,7 +11,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">sjPlot</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.8.16.3</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.8.17</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/dist_f.html b/reference/dist_f.html
index 59680f95..bb4e83fb 100644
--- a/reference/dist_f.html
+++ b/reference/dist_f.html
@@ -11,7 +11,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">sjPlot</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.8.16.3</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.8.17</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/dist_norm.html b/reference/dist_norm.html
index b9c8dcdc..931214f0 100644
--- a/reference/dist_norm.html
+++ b/reference/dist_norm.html
@@ -11,7 +11,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">sjPlot</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.8.16.3</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.8.17</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/dist_t.html b/reference/dist_t.html
index d7fbb331..5d808dac 100644
--- a/reference/dist_t.html
+++ b/reference/dist_t.html
@@ -11,7 +11,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">sjPlot</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.8.16.3</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.8.17</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/efc.html b/reference/efc.html
index f2222b45..a7ef2ef2 100644
--- a/reference/efc.html
+++ b/reference/efc.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">sjPlot</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.8.16.3</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.8.17</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/index.html b/reference/index.html
index 692a4a36..17204a41 100644
--- a/reference/index.html
+++ b/reference/index.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">sjPlot</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.8.16.3</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.8.17</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/plot_frq.html b/reference/plot_frq.html
index 59b82b85..926d27c4 100644
--- a/reference/plot_frq.html
+++ b/reference/plot_frq.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">sjPlot</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.8.16.3</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.8.17</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/plot_gpt.html b/reference/plot_gpt.html
index 6708c360..3581ae71 100644
--- a/reference/plot_gpt.html
+++ b/reference/plot_gpt.html
@@ -11,7 +11,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">sjPlot</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.8.16.3</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.8.17</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/plot_grid.html b/reference/plot_grid.html
index 2865d777..68787e5b 100644
--- a/reference/plot_grid.html
+++ b/reference/plot_grid.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">sjPlot</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.8.16.3</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.8.17</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/plot_grpfrq.html b/reference/plot_grpfrq.html
index 5860d758..b3d3f339 100644
--- a/reference/plot_grpfrq.html
+++ b/reference/plot_grpfrq.html
@@ -9,7 +9,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">sjPlot</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.8.16.3</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.8.17</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/plot_kfold_cv.html b/reference/plot_kfold_cv.html
index 6a1a136f..3ed49264 100644
--- a/reference/plot_kfold_cv.html
+++ b/reference/plot_kfold_cv.html
@@ -11,7 +11,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">sjPlot</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.8.16.3</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.8.17</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/plot_likert.html b/reference/plot_likert.html
index 6d384353..a83b4c79 100644
--- a/reference/plot_likert.html
+++ b/reference/plot_likert.html
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diff --git a/reference/plot_model.html b/reference/plot_model.html
index 24bdbfbf..a33e83cc 100644
--- a/reference/plot_model.html
+++ b/reference/plot_model.html
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diff --git a/reference/plot_models.html b/reference/plot_models.html
index 4fd90256..3f718b93 100644
--- a/reference/plot_models.html
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index e05717de..e69c23c1 100644
--- a/reference/plot_residuals.html
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index 55f4a170..aacda730 100644
--- a/reference/plot_scatter.html
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index 6e5aab78..838f7a0d 100644
--- a/reference/plot_stackfrq.html
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diff --git a/reference/plot_xtab.html b/reference/plot_xtab.html
index a08ffd1f..1b141be6 100644
--- a/reference/plot_xtab.html
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diff --git a/reference/save_plot.html b/reference/save_plot.html
index a481f131..75059d34 100644
--- a/reference/save_plot.html
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index 6a579e41..1903906a 100644
--- a/reference/set_theme.html
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diff --git a/reference/sjPlot-package.html b/reference/sjPlot-package.html
index 2af4515c..daaae1a8 100644
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index 320f0e60..2ce3dfc3 100644
--- a/reference/sjPlot-themes.html
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diff --git a/reference/sjp.aov1.html b/reference/sjp.aov1.html
index 7ba99eae..f31bad82 100644
--- a/reference/sjp.aov1.html
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diff --git a/reference/sjp.chi2.html b/reference/sjp.chi2.html
index a2645c49..94647ec0 100644
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diff --git a/reference/sjp.corr.html b/reference/sjp.corr.html
index 76aafe59..7288438b 100644
--- a/reference/sjp.corr.html
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index 0033e281..ae94b5eb 100644
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index 23c85b1c..8d8bba5b 100644
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index b177720e..56febb99 100644
--- a/reference/tab_fa.html
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index de282d6c..9c45dec0 100644
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index 3703e83c..0b930cc4 100644
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index 72ebe6ea..428405cf 100644
--- a/reference/tab_pca.html
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index cf53a852..b0e36ee5 100644
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index 8ea7d6f8..7ca72c94 100644
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diff --git a/search.json b/search.json
index f46bc4cd..854d771e 100644
--- a/search.json
+++ b/search.json
@@ -1 +1 @@
-[{"path":"https://strengejacke.github.io/sjPlot/CODE_OF_CONDUCT.html","id":null,"dir":"","previous_headings":"","what":"Contributor Code of Conduct","title":"Contributor Code of Conduct","text":"contributors maintainers project, pledge respect people contribute reporting issues, posting feature requests, updating documentation, submitting pull requests patches, activities. committed making participation project harassment-free experience everyone, regardless level experience, gender, gender identity expression, sexual orientation, disability, personal appearance, body size, race, ethnicity, age, religion. Examples unacceptable behavior participants include use sexual language imagery, derogatory comments personal attacks, trolling, public private harassment, insults, unprofessional conduct. Project maintainers right responsibility remove, edit, reject comments, commits, code, wiki edits, issues, contributions aligned Code Conduct. Project maintainers follow Code Conduct may removed project team. Instances abusive, harassing, otherwise unacceptable behavior may reported opening issue contacting one project maintainers. Code Conduct adapted Contributor Covenant (http://contributor-covenant.org), version 1.0.0, available http://contributor-covenant.org/version/1/0/0/","code":""},{"path":"https://strengejacke.github.io/sjPlot/CONTRIBUTING.html","id":null,"dir":"","previous_headings":"","what":"Contributing to sjmisc","title":"Contributing to sjmisc","text":"outlines propose change sjmisc.","code":""},{"path":"https://strengejacke.github.io/sjPlot/CONTRIBUTING.html","id":"fixing-typos","dir":"","previous_headings":"","what":"Fixing typos","title":"Contributing to sjmisc","text":"Small typos grammatical errors documentation may edited directly using GitHub web interface, long changes made source file. want fix typos documentation, please edit related .R file R/ folder. edit .Rd file man/.","code":""},{"path":"https://strengejacke.github.io/sjPlot/CONTRIBUTING.html","id":"filing-an-issue","dir":"","previous_headings":"","what":"Filing an issue","title":"Contributing to sjmisc","text":"easiest way propose change new feature file issue. ’ve found bug, may also create associated issue. possible, try illustrate proposal bug minimal reproducible example.","code":""},{"path":"https://strengejacke.github.io/sjPlot/CONTRIBUTING.html","id":"pull-request","dir":"","previous_headings":"","what":"Pull request","title":"Contributing to sjmisc","text":"Please create Git branch pull request (PR). contributed code roughly follow tidyverse style guide. Exceptions guide: separated, use underscores function names, dots argument names. See example set_na(). sjmisc uses roxygen2, Markdown syntax, documentation. sjmisc uses testthat. Adding tests PR makes easier merge PR code base. PR user-visible change, may add bullet top NEWS.md describing changes made. may optionally add GitHub username, links relevant issue(s)/PR(s).","code":""},{"path":"https://strengejacke.github.io/sjPlot/CONTRIBUTING.html","id":"code-of-conduct","dir":"","previous_headings":"","what":"Code of Conduct","title":"Contributing to sjmisc","text":"Please note project released Contributor Code Conduct. participating project agree abide terms.","code":""},{"path":"https://strengejacke.github.io/sjPlot/articles/blackwhitefigures.html","id":"barplots-in-grey-scaled-colors","dir":"Articles","previous_headings":"","what":"Barplots in grey-scaled colors","title":"Black & White Figures for Print Journals","text":"two ways create plots black white greyscale. bar plots, geom.colors = \"gs\" creates plot using greyscale (based scales::grey_pal()).","code":"library(sjPlot) library(sjmisc) library(sjlabelled) library(ggplot2) theme_set(theme_bw()) data(efc) plot_grpfrq(efc$e42dep, efc$c172code, geom.colors = \"gs\")"},{"path":"https://strengejacke.github.io/sjPlot/articles/blackwhitefigures.html","id":"lineplots-in-bw-with-different-linetypes","dir":"Articles","previous_headings":"","what":"Lineplots in b/w with different linetypes","title":"Black & White Figures for Print Journals","text":"Similar barplots, lineplots - mostly plot_model() - can plotted greyscale well (colors = \"gs\"). However, cases lines colored greyscale difficult distinguish. case, plot_model() supports black & white figures different linetypes. Use colors = \"bw\" create b/w-plot.  Different linetypes apply linetyped plots, usually plot single line - ’s need different linetypes, can just set colors = \"black\" (colors = \"bw\").","code":"# create binrary response y <- ifelse(efc$neg_c_7 < median(na.omit(efc$neg_c_7)), 0, 1)  # create data frame for fitting model df <- data.frame(   y = to_factor(y),   sex = to_factor(efc$c161sex),   dep = to_factor(efc$e42dep),   barthel = efc$barthtot,   education = to_factor(efc$c172code) )  # set variable label for response set_label(df$y) <- \"High Negative Impact\"  # fit model fit <- glm(y ~., data = df, family = binomial(link = \"logit\"))  # plot marginal effects plot_model(   fit,    type = \"pred\",    terms = c(\"barthel\", \"sex\",\"dep\"),    colors = \"bw\",   ci.lvl = NA ) # plot coefficients plot_model(fit, colors = \"black\")"},{"path":"https://strengejacke.github.io/sjPlot/articles/custplot.html","id":"tweaking-plot-appearance","dir":"Articles","previous_headings":"","what":"Tweaking plot appearance","title":"Customize Plot Appearance","text":"base function globally change theme option sjp-function set_theme(). Except geom-colors geom-sizes, theme-options can set via function. new theme applied following plots created sjPlot package. various arguments change colors, sizes, angles etc. labels. Following example show changes colors, sizes, angles, geom-outlines theme.","code":"# load libraries library(sjPlot)  # for plotting library(sjmisc)  # for sample data library(ggplot2) # to access ggplot-themes  # load sample data set data(efc)  set_theme(   geom.outline.color = \"antiquewhite4\",    geom.outline.size = 1,    geom.label.size = 2,   geom.label.color = \"grey50\",   title.color = \"red\",    title.size = 1.5,    axis.angle.x = 45,    axis.textcolor = \"blue\",    base = theme_bw() )  plot_grpfrq(   efc$e42dep,    efc$e16sex,    title = NULL,    geom.colors = c(\"cadetblue\", \"coral\"),    geom.size = 0.4 )"},{"path":"https://strengejacke.github.io/sjPlot/articles/custplot.html","id":"using-the-color-brewer-palettes","dir":"Articles","previous_headings":"","what":"Using the Color Brewer palettes","title":"Customize Plot Appearance","text":"plotting functions support usage Colorbrewer palettes. apply color brewer palette, use specify palette geom.colors. valid color brewer palette recognized automatically.  overview supported color codes can obtained display.brewer.() RColorBrewer package.","code":"# blank theme set_theme(   base = theme_blank(),   axis.title.size = .9,   axis.textsize = .9,   legend.size = .7,   legend.title.size = .8,   geom.label.size = 3 )  plot_grpfrq(   efc$e42dep,    efc$e15relat,    geom.colors = \"PuRd\",    show.values = FALSE ) library(RColorBrewer) display.brewer.all()"},{"path":"https://strengejacke.github.io/sjPlot/articles/custplot.html","id":"plot-with-flipped-coordinates","dir":"Articles","previous_headings":"","what":"Plot with flipped coordinates","title":"Customize Plot Appearance","text":"plot’s axes can flipped using coord.flip = TRUE. needed, labels can placed inside bars vjust hjust arguments. cases, might need adjust label colors geom.label.color = \"white\".","code":"set_theme(geom.label.color = \"white\", geom.label.size = 3)  # labels appear very large due to export metrics plot_grpfrq(efc$e42dep, efc$e16sex, coord.flip = TRUE)"},{"path":"https://strengejacke.github.io/sjPlot/articles/custplot.html","id":"adding-plot-margins","dir":"Articles","previous_headings":"","what":"Adding plot margins","title":"Customize Plot Appearance","text":"Plots margins towards axes may look strange people (, though). restore ggplot-default behaviour, use expand.grid argument:","code":"plot_grpfrq(efc$e42dep, efc$e16sex, expand.grid = TRUE)"},{"path":"https://strengejacke.github.io/sjPlot/articles/custplot.html","id":"theme-options","dir":"Articles","previous_headings":"","what":"Theme options","title":"Customize Plot Appearance","text":"can use pre-defined theme ggplot, like theme_bw(), theme_classic() theme_minimal() default theme.","code":"set_theme(base = theme_light()) plot_frq(efc$e42dep)"},{"path":"https://strengejacke.github.io/sjPlot/articles/custplot.html","id":"pre-defined-themes","dir":"Articles","previous_headings":"","what":"Pre-defined themes","title":"Customize Plot Appearance","text":"set pre-defined themes sjPlot-package. See ?\"sjPlot-themes\" complete list.","code":"library(sjmisc) data(efc) efc <- to_factor(efc, e42dep, c172code) m <- lm(neg_c_7 ~ pos_v_4 + c12hour + e42dep + c172code, data = efc)  # reset theme set_theme(base = theme_grey())  # forest plot of regression model p <- plot_model(m)  # default theme p # pre-defined theme p + theme_sjplot()"},{"path":"https://strengejacke.github.io/sjPlot/articles/custplot.html","id":"pre-defined-scales","dir":"Articles","previous_headings":"","what":"Pre-defined scales","title":"Customize Plot Appearance","text":"also new scale ggplot-objects, scale_color_sjplot() scale_fill_sjplot().  see currently available color sets, use show_sjplot_pals().","code":"p +    theme_sjplot2() +    scale_color_sjplot(\"simply\") show_sjplot_pals()"},{"path":"https://strengejacke.github.io/sjPlot/articles/custplot.html","id":"set-up-own-themes-based-on-existing-themes","dir":"Articles","previous_headings":"","what":"Set up own themes based on existing themes","title":"Customize Plot Appearance","text":"want use specific theme base building theme, use base argument. using base instead theme, arguments settings colors etc. ignored.","code":"set_theme(base = theme_bw(), axis.linecolor = \"darkgreen\") plot_frq(efc$e42dep)"},{"path":"https://strengejacke.github.io/sjPlot/articles/custplot.html","id":"further-customization-options","dir":"Articles","previous_headings":"","what":"Further customization options","title":"Customize Plot Appearance","text":"plotting function invisibly returns ggplot-object. can add options customize appearance plot, like following example, count axis hidden (color set white):","code":"set_theme(   base = theme_classic(),   axis.tickslen = 0, # hides tick marks   axis.title.size = .9,   axis.textsize = .9,   legend.size = .7,   legend.title.size = .8,   geom.label.size = 3.5 )    plot_grpfrq(   efc$e42dep,   efc$e16sex,   coord.flip = TRUE,   show.axis.values = FALSE ) +   theme(axis.line.x = element_line(color = \"white\"))"},{"path":"https://strengejacke.github.io/sjPlot/articles/custplot.html","id":"plot-legend","dir":"Articles","previous_headings":"","what":"Plot legend","title":"Customize Plot Appearance","text":"plot’s legend can customized via various legend.-arguments, see following examples:","code":"set_theme(   base = theme_classic(),    legend.title.face = \"italic\", # title font face   legend.inside = TRUE,         # legend inside plot   legend.color = \"grey50\",      # legend label color   legend.pos = \"bottom right\",  # legend position inside plot   axis.title.size = .9,   axis.textsize = .9,   legend.size = .7,   legend.title.size = .8,   geom.label.size = 3 )  plot_grpfrq(efc$e42dep, efc$e16sex, coord.flip = TRUE) set_theme(   base = theme_classic(),    axis.linecolor = \"white\",     # \"remove\" axis lines   axis.textcolor.y = \"darkred\", # set axis label text only for y axis   axis.tickslen = 0,            # \"remove\" tick marks   legend.title.color = \"red\",   # legend title color   legend.title.size = 2,        # legend title size   legend.color = \"green\",       # legend label color   legend.pos = \"top\",           # legend position above plot   axis.title.size = .9,   axis.textsize = .9,   legend.size = .7,   geom.label.size = 3 )  plot_grpfrq(efc$e42dep, efc$e16sex)"},{"path":"https://strengejacke.github.io/sjPlot/articles/plot_interactions.html","id":"two-way-interactions","dir":"Articles","previous_headings":"","what":"Two-Way-Interactions","title":"Plotting Interaction Effects of Regression Models","text":"Note: better understand principle plotting interaction terms, might helpful read vignette marginal effects first. plot marginal effects interaction terms, least two model terms need specified (terms define interaction) terms-argument, effects computed. plot marginal effects three-way-interactions, three terms need specified terms. convenient way automatically plot interactions type = \"int\", scans model formula interaction terms uses terms-argument.  type = \"int\", terms need specified. Note plot type automatically uses first interaction term formula x-axis, second term used grouping factor. Furthermore, continuous variables used second term, can specify preset-values term mdrt.values-argument, used grouping levels. example, second term factor two levels (male/female), need choosing specific values moderator.  switch terms, example barthtot c161sex, simply switch order terms terms-argument use type = \"pred\".  switch terms plot-type type = \"int\", need re-fit model change formula accordingly, .e. using c161sex first term interaction.  default, continuous variables, minimum maximum values chosen grouping levels, 0 100 - ’s previous two plots identical. options well, e.g. mean-value +/- 1 standard deviation (suggested Cohen Cohen continuous variables popularized Aiken West 1991), can specified using mdrt.values.","code":"library(sjPlot) library(sjmisc) library(ggplot2) data(efc) theme_set(theme_sjplot())  # make categorical efc$c161sex <- to_factor(efc$c161sex)  # fit model with interaction fit <- lm(neg_c_7 ~ c12hour + barthtot * c161sex, data = efc)  plot_model(fit, type = \"pred\", terms = c(\"barthtot\", \"c161sex\")) plot_model(fit, type = \"int\") plot_model(fit, type = \"pred\", terms = c(\"c161sex\", \"barthtot [0, 100]\")) # fit model with interaction, switching terms in formula fit <- lm(neg_c_7 ~ c12hour + c161sex * barthtot, data = efc) plot_model(fit, type = \"int\") plot_model(fit, type = \"int\", mdrt.values = \"meansd\")"},{"path":"https://strengejacke.github.io/sjPlot/articles/plot_interactions.html","id":"three-way-interactions","dir":"Articles","previous_headings":"","what":"Three-Way-Interactions","title":"Plotting Interaction Effects of Regression Models","text":"Since terms-argument accepts three model terms, can also compute marginal effects 3-way-interaction.  , type = \"int\" automatically plot interaction terms, however, using mdrt.values = \"minmax\" default - case, “levels” 0 100 continuous variable barthtot chosen default.","code":"# fit model with 3-way-interaction fit <- lm(neg_c_7 ~ c12hour * barthtot * c161sex, data = efc)  # select only levels 30, 50 and 70 from continuous variable Barthel-Index plot_model(fit, type = \"pred\", terms = c(\"c12hour\", \"barthtot [30,50,70]\", \"c161sex\")) plot_model(fit, type = \"int\") #> [[1]] #>  #> [[2]] #>  #> [[3]] #>  #> [[4]]"},{"path":"https://strengejacke.github.io/sjPlot/articles/plot_interactions.html","id":"references","dir":"Articles","previous_headings":"","what":"References","title":"Plotting Interaction Effects of Regression Models","text":"Aiken West (1991). Multiple Regression: Testing Interpreting Interactions.","code":""},{"path":"https://strengejacke.github.io/sjPlot/articles/plot_marginal_effects.html","id":"marginal-effects","dir":"Articles","previous_headings":"","what":"Marginal effects","title":"Plotting Marginal Effects of Regression Models","text":"plot_model(type = \"pred\") computes predicted values possible levels values model’s predictors. simplest case, fitted model passed first argument, followed type argument term question terms argument:  plot shows predicted values response value term c12hour.","code":"library(sjPlot) library(ggplot2) data(efc) theme_set(theme_sjplot())  fit <- lm(barthtot ~ c12hour + neg_c_7 + c161sex + c172code, data = efc)  plot_model(fit, type = \"pred\", terms = \"c12hour\")"},{"path":"https://strengejacke.github.io/sjPlot/articles/plot_marginal_effects.html","id":"marginal-effects-for-different-groups","dir":"Articles","previous_headings":"Marginal effects","what":"Marginal effects for different groups","title":"Plotting Marginal Effects of Regression Models","text":"terms-argument accepts three model terms, second third term indicate grouping levels. allows predictions term question different levels model terms:  second grouping structure can defined, create plot multiple panels grid layout:","code":"plot_model(fit, type = \"pred\", terms = c(\"c12hour\", \"c172code\")) plot_model(fit, type = \"pred\", terms = c(\"c12hour\", \"c172code\", \"c161sex\"))"},{"path":"https://strengejacke.github.io/sjPlot/articles/plot_marginal_effects.html","id":"marginal-effects-at-specific-values-or-levels","dir":"Articles","previous_headings":"Marginal effects","what":"Marginal effects at specific values or levels","title":"Plotting Marginal Effects of Regression Models","text":"terms-argument defines model terms interest, model term defines grouping structure can limited certain values. allows compute plot marginal effects terms specific values . define values, put square brackets directly term name: terms = c(\"c12hour [30, 50, 80]\", \"c172code [1,3]\")  Note plot, although values 30, 50 80 selected c12hour, continuous scale automatically adds panel grids every 5 units along x-axis. Defining values especially useful variables , instance, log-transformed. plot_model() typically uses range log-transformed variable, cases want. situation, specify range terms-argument.   brackets terms-argument also accept name valid function, (back-)transform predicted valued. example, alternative specify values exponentiated, indicated [exp] terms-argument:","code":"plot_model(fit, type = \"pred\", terms = c(\"c12hour [30, 50, 80]\", \"c172code [1,3]\")) data(mtcars) mpg_model <- lm(mpg ~ log(hp), data = mtcars)  # x-values and predictions based on the log(hp)-values plot_model(mpg_model, type = \"pred\", terms = \"hp\") # x-values and predictions based on hp-values from 50 to 150 plot_model(mpg_model, type = \"pred\", terms = \"hp [50:150]\") # x-values and predictions based on exponentiated hp-values plot_model(mpg_model, type = \"pred\", terms = \"hp [exp]\")"},{"path":"https://strengejacke.github.io/sjPlot/articles/plot_marginal_effects.html","id":"polynomial-terms-and-splines","dir":"Articles","previous_headings":"Marginal effects","what":"Polynomial terms and splines","title":"Plotting Marginal Effects of Regression Models","text":"function also works models polynomial terms splines. Following code reproduces plot ?splines::bs:","code":"library(splines) data(women)  fm1 <- lm(weight ~ bs(height, df = 5), data = women) plot_model(fm1, type = \"pred\", terms = \"height\")"},{"path":"https://strengejacke.github.io/sjPlot/articles/plot_marginal_effects.html","id":"different-constant-values-for-factors","dir":"Articles","previous_headings":"Marginal effects","what":"Different constant values for factors","title":"Plotting Marginal Effects of Regression Models","text":"Model predictions based possible combinations model terms, - roughly speaking - created using expand.grid(). terms question, values used combinations. model predictors specified terms-argument, held constant (achieved sjstats::typical_value()). default, continuous variables set mean, factors set reference level.  However, one may want set factors proportions instead reference level. E.g., factor gender value 0 female value 1 male persons, set 0 marginal effects computed type = \"pred\". 40% sample female persons, another possibility hold factor constant use value .4 (reflecting proportion 40%). required, use type = \"eff\", internally call predict() compute marginal effects, rather effects::effect().","code":"data(efc) efc$c172code <- sjlabelled::as_factor(efc$c172code) fit <- lm(neg_c_7 ~ c12hour + c172code, data = efc)  # reference category is used for \"c172code\", i.e. c172code # used the first level as value for predictions plot_model(fit, type = \"pred\", terms = \"c12hour\") # proportion is used for \"c172code\", i.e. it is set to # mean(sjlabelled::as_numeric(efc$c172code), na.rm = T), # which is about 1.9715 plot_model(fit, type = \"eff\", terms = \"c12hour\")"},{"path":"https://strengejacke.github.io/sjPlot/articles/plot_marginal_effects.html","id":"interaction-terms","dir":"Articles","previous_headings":"","what":"Interaction terms","title":"Plotting Marginal Effects of Regression Models","text":"Plotting interaction terms described separate vignette.","code":""},{"path":"https://strengejacke.github.io/sjPlot/articles/plot_model_estimates.html","id":"fitting-a-logistic-regression-model","dir":"Articles","previous_headings":"","what":"Fitting a logistic regression model","title":"Plotting Estimates (Fixed Effects) of Regression Models","text":"First, fit model used following examples. examples work way model well.","code":"# create binary response y <- ifelse(efc$neg_c_7 < median(na.omit(efc$neg_c_7)), 0, 1)  # create data frame for fitting model df <- data.frame(   y = to_factor(y),   sex = to_factor(efc$c161sex),   dep = to_factor(efc$e42dep),   barthel = efc$barthtot,   education = to_factor(efc$c172code) )  # set variable label for response set_label(df$y) <- \"High Negative Impact\"  # fit model m1 <- glm(y ~., data = df, family = binomial(link = \"logit\"))"},{"path":"https://strengejacke.github.io/sjPlot/articles/plot_model_estimates.html","id":"plotting-estimates-of-generalized-linear-models","dir":"Articles","previous_headings":"","what":"Plotting estimates of generalized linear models","title":"Plotting Estimates (Fixed Effects) of Regression Models","text":"simplest function call just passing model object argument. default, estimates sorted descending order, highest effect top.  “neutral” line, .e. vertical intercept indicates effect (x-axis position 1 glm’s position 0 linear models), drawn slightly thicker grid lines. can change line color vline.color-argument.","code":"plot_model(m1) plot_model(m1, vline.color = \"red\")"},{"path":"https://strengejacke.github.io/sjPlot/articles/plot_model_estimates.html","id":"sorting-estimates","dir":"Articles","previous_headings":"","what":"Sorting estimates","title":"Plotting Estimates (Fixed Effects) of Regression Models","text":"default, estimates sorted order introduced model. Use sort.est = TRUE sort estimates descending order, highest lowest value.  Another way sort estimates use order.terms-argument. numeric vector, indicating order estimates plot. summary, see “sex2” first term, followed three dependency-categories (position 2-4), Barthel-Index (5) two levels intermediate high level education (6 7). Now want educational levels (6 7) first, gender (1), followed dependency (2-4)finally Barthel-Index (5). Use order numeric vector order.terms-argument.","code":"plot_model(m1, sort.est = TRUE) summary(m1) #>  #> Call: #> glm(formula = y ~ ., family = binomial(link = \"logit\"), data = df) #>  #> Coefficients: #>              Estimate Std. Error z value Pr(>|z|)     #> (Intercept)  0.700232   0.576715   1.214 0.224682     #> sex2         0.649136   0.186186   3.486 0.000489 *** #> dep2         0.485259   0.361498   1.342 0.179480     #> dep3         1.125130   0.361977   3.108 0.001882 **  #> dep4         0.910194   0.441774   2.060 0.039368 *   #> barthel     -0.029802   0.004732  -6.298 3.02e-10 *** #> education2   0.226525   0.200298   1.131 0.258081     #> education3   0.283600   0.249327   1.137 0.255346     #> --- #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #>  #> (Dispersion parameter for binomial family taken to be 1) #>  #>     Null deviance: 1122.16  on 814  degrees of freedom #> Residual deviance:  939.77  on 807  degrees of freedom #>   (93 observations deleted due to missingness) #> AIC: 955.77 #>  #> Number of Fisher Scoring iterations: 4 plot_model(m1, order.terms = c(6, 7, 1, 2, 3, 4, 5))"},{"path":"https://strengejacke.github.io/sjPlot/articles/plot_model_estimates.html","id":"estimates-on-the-untransformed-scale","dir":"Articles","previous_headings":"","what":"Estimates on the untransformed scale","title":"Plotting Estimates (Fixed Effects) of Regression Models","text":"default, plot_model() automatically exponentiates coefficients, appropriate (e.g. models log logit link). can explicitley prevent transformation setting transform-argument NULL, apply transformation using character vector function name.","code":"plot_model(m1, transform = NULL) plot_model(m1, transform = \"plogis\")"},{"path":"https://strengejacke.github.io/sjPlot/articles/plot_model_estimates.html","id":"showing-value-labels","dir":"Articles","previous_headings":"","what":"Showing value labels","title":"Plotting Estimates (Fixed Effects) of Regression Models","text":"default, just dots error bars plotted. Use show.values = TRUE show value labels estimates values, use show.p = FALSE suppress asterisks indicate significance level p-values. Use value.offset adjust relative positioning value labels dots lines.","code":"plot_model(m1, show.values = TRUE, value.offset = .3)"},{"path":"https://strengejacke.github.io/sjPlot/articles/plot_model_estimates.html","id":"labelling-the-plot","dir":"Articles","previous_headings":"","what":"Labelling the plot","title":"Plotting Estimates (Fixed Effects) of Regression Models","text":"seen examples, default, plotting-functions sjPlot retrieve value variable labels data labelled, using sjlabelled-package. data labelled, variable names used. cases, use arguments title, axis.labels axis.title annotate plot title axes. want variable names instead labels, even labelled data, use \"\" argument-value, e.g. axis.labels = \"\", set auto.label FALSE. Furthermore, plot_model() applies case-conversion labels default, using snakecase-package. converts labels human-readable versions. Use case = NULL turn case-conversion , refer package-vignette snakecase-package options.","code":"data(iris) m2 <- lm(Sepal.Length ~ Sepal.Width + Petal.Length + Species, data = iris)  # variable names as labels, but made \"human readable\" # separating dots are removed plot_model(m2) # to use variable names even for labelled data plot_model(m1, axis.labels = \"\", title = \"my own title\")"},{"path":"https://strengejacke.github.io/sjPlot/articles/plot_model_estimates.html","id":"pick-or-remove-specific-terms-from-plot","dir":"Articles","previous_headings":"","what":"Pick or remove specific terms from plot","title":"Plotting Estimates (Fixed Effects) of Regression Models","text":"Use terms resp. rm.terms select specific terms () plotted.","code":"# keep only coefficients sex2, dep2 and dep3 plot_model(m1, terms = c(\"sex2\", \"dep2\", \"dep3\")) # remove coefficients sex2, dep2 and dep3 plot_model(m1, rm.terms = c(\"sex2\", \"dep2\", \"dep3\"))"},{"path":"https://strengejacke.github.io/sjPlot/articles/plot_model_estimates.html","id":"standardized-estimates","dir":"Articles","previous_headings":"","what":"Standardized estimates","title":"Plotting Estimates (Fixed Effects) of Regression Models","text":"linear models, can also plot standardized beta coefficients, using type = \"std\" type = \"std2\". two options differ way coefficients standardized. type = \"std2\" plots standardized beta values, however, standardization follows Gelman’s (2008) suggestion, rescaling estimates dividing two standard deviations instead just one.","code":"plot_model(m2, type = \"std\")"},{"path":"https://strengejacke.github.io/sjPlot/articles/plot_model_estimates.html","id":"bayesian-models-fitted-with-stan","dir":"Articles","previous_headings":"","what":"Bayesian models (fitted with Stan)","title":"Plotting Estimates (Fixed Effects) of Regression Models","text":"plot_model() also supports stan-models fitted rstanarm brms packages. However, differences compared previous plot examples. First, course, confidence intervals, uncertainty intervals - high density intervals, precise. Second, ’s just one interval range, inner outer probability. default, inner probability fixed .5 (50%), outer probability specified via ci.lvl (defaults .89 (89%) Bayesian models). However, can also use arguments prob.inner prob.outer define intervals boundaries. Third, point estimate default median, can also another value, like mean. can specified bpe-argument.","code":"if (require(\"rstanarm\", quietly = TRUE)) {   # make sure we apply a nice theme   library(ggplot2)   theme_set(theme_sjplot())      data(mtcars)   m <- stan_glm(mpg ~ wt + am + cyl + gear, data = mtcars, chains = 1)      # default model   plot_model(m)   # same model, with mean point estimate, dot-style for point estimate   # and different inner/outer probabilities of the HDI   plot_model(     m,      bpe = \"mean\",     bpe.style = \"dot\",     prob.inner = .4,     prob.outer = .8   ) }"},{"path":"https://strengejacke.github.io/sjPlot/articles/plot_model_estimates.html","id":"tweaking-plot-appearance","dir":"Articles","previous_headings":"","what":"Tweaking plot appearance","title":"Plotting Estimates (Fixed Effects) of Regression Models","text":"several options customize plot appearance: colors-argument either takes name valid colorbrewer palette (see also related vignette), \"bw\" \"gs\" black/white greyscaled colors, string color name. value.offset value.size adjust positioning size value labels, shown. dot.size line.size change size dots error bars. vline.color changes neutral “intercept” line. width, alpha scale passed certain ggplot-geoms, like geom_errorbar() geom_density_ridges().","code":"plot_model(   m1,    colors = \"Accent\",    show.values = TRUE,   value.offset = .4,   value.size = 4,   dot.size = 3,   line.size = 1.5,   vline.color = \"blue\",   width = 1.5 )"},{"path":"https://strengejacke.github.io/sjPlot/articles/plot_model_estimates.html","id":"references","dir":"Articles","previous_headings":"","what":"References","title":"Plotting Estimates (Fixed Effects) of Regression Models","text":"Gelman (2008) Scaling regression inputs dividing two standard deviations. Statistics Medicine 27: 2865–2873.","code":""},{"path":"https://strengejacke.github.io/sjPlot/articles/sjtitemanalysis.html","id":"performing-an-item-analysis-of-a-scale-or-index","dir":"Articles","previous_headings":"","what":"Performing an item analysis of a scale or index","title":"Item Analysis of a Scale or an Index","text":"function performs item analysis certain statistics useful scale index development. Following statistics computed variable (column) data frame: percentage missing values mean value standard deviation skew item difficulty item discrimination Cronbach’s Alpha item removed scale mean (average) inter-item-correlation Optional, following statistics can computed well: kurstosis Shapiro-Wilk Normality Test argument factor.groups NULL, data frame df splitted groups, assuming factor.groups indicate columns (variables) data frame belong certain factor (see, instance, return value function tab_pca() parameters::principal_components() example retrieving factor groups scale). useful perfomed principal component analysis factor analysis first step, now want see whether found factors / components represent scale index score. demonstrate function, first need data:","code":""},{"path":"https://strengejacke.github.io/sjPlot/articles/sjtitemanalysis.html","id":"index-score-with-one-component","dir":"Articles","previous_headings":"","what":"Index score with one component","title":"Item Analysis of a Scale or an Index","text":"simplest function call just passing data frame argument. case, function assumes variables data frame belong one factor . Component 1 interprete output, may consider following values rule--thumbs indicating reliable scale: item difficulty range 0.2 0.8. Ideal value p+(1-p)/2 (mostly 0.5 0.8) item discrimination, acceptable values 0.2 higher; closer 1 better case total Cronbach’s Alpha value acceptable cut-0.7 (mostly index items), mean inter-item-correlation alternative measure indicate acceptability; satisfactory range lies 0.2 0.4","code":"tab_itemscale(mydf)"},{"path":"https://strengejacke.github.io/sjPlot/articles/sjtitemanalysis.html","id":"index-score-with-more-than-one-component","dir":"Articles","previous_headings":"","what":"Index score with more than one component","title":"Item Analysis of a Scale or an Index","text":"items COPE index used example represent single factor. can check , instance, principle component analysis. know, variable belongs factor (.e. variable part component), can pass numeric vector group indices argument factor.groups. case, data frame divided components specified factor.groups, component (factor) analysed. PCA extracted two components. Now tab_itemscale() … performs item analysis components, showing whether reliable useful scale index score builds index component, standardizing scale adds component-correlation-matrix, see whether index scores (based components) highly correlated . Component 1   Component 2  ","code":"library(parameters) # Compute PCA on Cope-Index, and retrieve  # factor indices for each COPE index variable pca <- parameters::principal_components(mydf) factor.groups <- parameters::closest_component(pca) tab_itemscale(mydf, factor.groups) #> Warning: Data frame needs at least three columns for reliability-test."},{"path":"https://strengejacke.github.io/sjPlot/articles/sjtitemanalysis.html","id":"adding-further-statistics","dir":"Articles","previous_headings":"","what":"Adding further statistics","title":"Item Analysis of a Scale or an Index","text":"Component 1   Component 2  ","code":"tab_itemscale(mydf, factor.groups, show.shapiro = TRUE, show.kurtosis = TRUE) #> Warning: Data frame needs at least three columns for reliability-test."},{"path":"https://strengejacke.github.io/sjPlot/articles/tab_bayes.html","id":"bayesian-models-summaries-as-html-table","dir":"Articles","previous_headings":"","what":"Bayesian models summaries as HTML table","title":"Summary of Bayesian Models as HTML Table","text":"Bayesian regression models, differences table output simple models mixed models tab_models() use Highest Density Intervals instead confidence intervals, Bayes-R-squared values, different “point estimate” (, default, median posterior draws).","code":"tab_model(m1)"},{"path":"https://strengejacke.github.io/sjPlot/articles/tab_bayes.html","id":"multivariate-response-models","dir":"Articles","previous_headings":"","what":"Multivariate response models","title":"Summary of Bayesian Models as HTML Table","text":"multivariate response models, like mediator-analysis-models, recommended print just one model table, regression displayed “model” output.","code":"tab_model(m2)"},{"path":"https://strengejacke.github.io/sjPlot/articles/tab_bayes.html","id":"show-two-credible-interval-column","dir":"Articles","previous_headings":"","what":"Show two Credible Interval-column","title":"Summary of Bayesian Models as HTML Table","text":"show second CI-column, use show.ci50 = TRUE.","code":"tab_model(m2, show.ci50 = TRUE)"},{"path":"https://strengejacke.github.io/sjPlot/articles/tab_bayes.html","id":"mixing-multivariate-and-univariate-response-models","dir":"Articles","previous_headings":"","what":"Mixing multivariate and univariate response models","title":"Summary of Bayesian Models as HTML Table","text":"multivariate univariate response models displayed one table, column Response added multivariate response model, indicate different outcomes.","code":"tab_model(m1, m2)"},{"path":"https://strengejacke.github.io/sjPlot/articles/tab_mixed.html","id":"mixed-models-summaries-as-html-table","dir":"Articles","previous_headings":"","what":"Mixed models summaries as HTML table","title":"Summary of Mixed Models as HTML Table","text":"Unlike tables non-mixed models, tab_models() adds additional information random effects table output mixed models. can hide information show.icc = FALSE show.re.var = FALSE. Furthermore, R-squared values marginal conditional R-squared statistics, based Nakagawa et al. 2017. marginal R-squared considers variance fixed effects, conditional R-squared takes fixed random effects account. p-value simple approximation, based t-statistics using normal distribution function. precise p-value can computed using p.val = \"kr\". case, applies linear mixed models, computation p-values based conditional F-tests Kenward-Roger approximation degrees freedom (using using pbkrtest-package). Note computation time consuming thus used default. can also display approximated degrees freedom show.df.","code":"m1 <- lmer(neg_c_7 ~ c160age + c161sex + e42dep + (1 | cluster), data = efc) m2 <- lmer(Reaction ~ Days + (1 + Days | Subject), data = sleepstudy)  tab_model(m1, m2) tab_model(m1, p.val = \"kr\", show.df = TRUE)"},{"path":"https://strengejacke.github.io/sjPlot/articles/tab_mixed.html","id":"generalized-linear-mixed-models","dir":"Articles","previous_headings":"","what":"Generalized linear mixed models","title":"Summary of Mixed Models as HTML Table","text":"tab_model() can also print combine models different link-functions.","code":"data(\"efc\") efc$neg_c_7d <- ifelse(efc$neg_c_7 < median(efc$neg_c_7, na.rm = TRUE), 0, 1) efc$cluster <- as.factor(efc$e15relat) m3 <- glmer(   neg_c_7d ~ c160age + c161sex + e42dep + (1 | cluster),   data = efc,    family = binomial(link = \"logit\") )  tab_model(m1, m3)"},{"path":"https://strengejacke.github.io/sjPlot/articles/tab_mixed.html","id":"more-complex-models","dir":"Articles","previous_headings":"","what":"More complex models","title":"Summary of Mixed Models as HTML Table","text":"Finally, example glmmTMB-package show easy print zero-inflated generalized linear mixed models HTML table.","code":"library(glmmTMB) data(\"Salamanders\") m4 <- glmmTMB(   count ~ spp + mined + (1 | site),   ziformula = ~ spp + mined,    family = truncated_poisson(link = \"log\"),    data = Salamanders )  tab_model(m1, m3, m4, show.ci = FALSE)"},{"path":"https://strengejacke.github.io/sjPlot/articles/tab_mixed.html","id":"references","dir":"Articles","previous_headings":"","what":"References","title":"Summary of Mixed Models as HTML Table","text":"Nakagawa S, Johnson P, Schielzeth H (2017) coefficient determination R2 intra-class correlation coefficient generalized linear mixed-effects models revisted expanded. J. R. Soc. Interface 14. doi: 10.1098/rsif.2017.0213","code":""},{"path":"https://strengejacke.github.io/sjPlot/articles/tab_model_estimates.html","id":"a-simple-html-table-from-regression-results","dir":"Articles","previous_headings":"","what":"A simple HTML table from regression results","title":"Summary of Regression Models as HTML Table","text":"First, fit two linear models demonstrate tab_model()-function. simplest way producing table output passing fitted model parameter. default, estimates, confidence intervals (CI) p-values (p) reported. summary, numbers observations well R-squared values shown.","code":"m1 <- lm(barthtot ~ c160age + c12hour + c161sex + c172code, data = efc) m2 <- lm(neg_c_7 ~ c160age + c12hour + c161sex + e17age, data = efc) tab_model(m1)"},{"path":"https://strengejacke.github.io/sjPlot/articles/tab_model_estimates.html","id":"automatic-labelling","dir":"Articles","previous_headings":"","what":"Automatic labelling","title":"Summary of Regression Models as HTML Table","text":"sjPlot-packages features labelled data, coefficients table already labelled example. name dependent variable(s) used main column header model. non-labelled data, coefficient names shown. factors involved auto.label = TRUE, “pretty” parameters names used (see format_parameters().","code":"data(mtcars) m.mtcars <- lm(mpg ~ cyl + hp + wt, data = mtcars) tab_model(m.mtcars) set.seed(2) dat <- data.frame(   y = runif(100, 0, 100),   drug = as.factor(sample(c(\"nonsense\", \"useful\", \"placebo\"), 100, TRUE)),   group = as.factor(sample(c(\"control\", \"treatment\"), 100, TRUE)) )  pretty_names <- lm(y ~ drug * group, data = dat) tab_model(pretty_names)"},{"path":"https://strengejacke.github.io/sjPlot/articles/tab_model_estimates.html","id":"turn-off-automatic-labelling","dir":"Articles","previous_headings":"Automatic labelling","what":"Turn off automatic labelling","title":"Summary of Regression Models as HTML Table","text":"turn automatic labelling, use auto.label = FALSE, provide empty character vector pred.labels dv.labels. models non-labelled data factors.","code":"tab_model(m1, auto.label = FALSE) tab_model(pretty_names, auto.label = FALSE)"},{"path":"https://strengejacke.github.io/sjPlot/articles/tab_model_estimates.html","id":"more-than-one-model","dir":"Articles","previous_headings":"","what":"More than one model","title":"Summary of Regression Models as HTML Table","text":"tab_model() can print multiple models , printed side--side. Identical coefficients matched row.","code":"tab_model(m1, m2)"},{"path":"https://strengejacke.github.io/sjPlot/articles/tab_model_estimates.html","id":"generalized-linear-models","dir":"Articles","previous_headings":"","what":"Generalized linear models","title":"Summary of Regression Models as HTML Table","text":"generalized linear models, ouput slightly adapted. Instead Estimates, column named Odds Ratios, Incidence Rate Ratios etc., depending model. coefficients case automatically converted (exponentiated). Furthermore, pseudo R-squared statistics shown summary.","code":"m3 <- glm(   tot_sc_e ~ c160age + c12hour + c161sex + c172code,    data = efc,   family = poisson(link = \"log\") )  efc$neg_c_7d <- ifelse(efc$neg_c_7 < median(efc$neg_c_7, na.rm = TRUE), 0, 1) m4 <- glm(   neg_c_7d ~ c161sex + barthtot + c172code,   data = efc,   family = binomial(link = \"logit\") )  tab_model(m3, m4)"},{"path":"https://strengejacke.github.io/sjPlot/articles/tab_model_estimates.html","id":"untransformed-estimates-on-the-linear-scale","dir":"Articles","previous_headings":"Generalized linear models","what":"Untransformed estimates on the linear scale","title":"Summary of Regression Models as HTML Table","text":"plot estimates linear scale, use transform = NULL.","code":"tab_model(m3, m4, transform = NULL, auto.label = FALSE)"},{"path":"https://strengejacke.github.io/sjPlot/articles/tab_model_estimates.html","id":"more-complex-models","dir":"Articles","previous_headings":"","what":"More complex models","title":"Summary of Regression Models as HTML Table","text":"models, like hurdle- zero-inflated models, also work tab_model(). case, zero inflation model indicated table. Use show.zeroinf = FALSE hide part table. can combine model one table.","code":"library(pscl) data(\"bioChemists\") m5 <- zeroinfl(art ~ fem + mar + kid5 + ment | kid5 + phd + ment, data = bioChemists)  tab_model(m5) tab_model(m1, m3, m5, auto.label = FALSE, show.ci = FALSE)"},{"path":"https://strengejacke.github.io/sjPlot/articles/tab_model_estimates.html","id":"show-or-hide-further-columns","dir":"Articles","previous_headings":"","what":"Show or hide further columns","title":"Summary of Regression Models as HTML Table","text":"tab_model() argument allow show hide specific columns output: show.est show/hide column model estimates. show.ci show/hide column confidence intervals. show.se show/hide column standard errors. show.std show/hide column standardized estimates (standard errors). show.p show/hide column p-values. show.stat show/hide column coefficients’ test statistics. show.df linear mixed models, p-values based degrees freedom Kenward-Rogers approximation, degrees freedom shown.","code":""},{"path":"https://strengejacke.github.io/sjPlot/articles/tab_model_estimates.html","id":"adding-columns","dir":"Articles","previous_headings":"Show or hide further columns","what":"Adding columns","title":"Summary of Regression Models as HTML Table","text":"following example, standard errors, standardized coefficients test statistics also shown.","code":"tab_model(m1, show.se = TRUE, show.std = TRUE, show.stat = TRUE)"},{"path":"https://strengejacke.github.io/sjPlot/articles/tab_model_estimates.html","id":"removing-columns","dir":"Articles","previous_headings":"Show or hide further columns","what":"Removing columns","title":"Summary of Regression Models as HTML Table","text":"following example, default columns removed.","code":"tab_model(m3, m4, show.ci = FALSE, show.p = FALSE, auto.label = FALSE)"},{"path":"https://strengejacke.github.io/sjPlot/articles/tab_model_estimates.html","id":"removing-and-sorting-columns","dir":"Articles","previous_headings":"Show or hide further columns","what":"Removing and sorting columns","title":"Summary of Regression Models as HTML Table","text":"Another way remove columns, also allows reorder columns, col.order-argument. character vector, element indicates column output. value \"est\", instance, indicates estimates, \"std.est\" column standardized estimates . default, col.order contains possible columns. columns shown (see previous tables, example using show.se = TRUE show standard errors, show.st = TRUE show standardized estimates) printed default. Colums excluded col.order shown, matter show*-arguments TRUE FALSE. show.se = TRUE, butcol.order contain element \"se\", standard errors shown. hand, show.est = FALSE, col.order include element \"est\", columns estimates shown. summary, col.order can used exclude columns table change order colums.","code":"tab_model(   m1, show.se = TRUE, show.std = TRUE, show.stat = TRUE,   col.order = c(\"p\", \"stat\", \"est\", \"std.se\", \"se\", \"std.est\") )"},{"path":"https://strengejacke.github.io/sjPlot/articles/tab_model_estimates.html","id":"collapsing-columns","dir":"Articles","previous_headings":"Show or hide further columns","what":"Collapsing columns","title":"Summary of Regression Models as HTML Table","text":"collapse.ci collapse.se, columns confidence intervals standard errors can collapsed one column together estimates. Sometimes table layout required.","code":"tab_model(m1, collapse.ci = TRUE)"},{"path":"https://strengejacke.github.io/sjPlot/articles/tab_model_estimates.html","id":"defining-own-labels","dir":"Articles","previous_headings":"","what":"Defining own labels","title":"Summary of Regression Models as HTML Table","text":"different options change labels column headers coefficients, e.g. : pred.labels change names coefficients Predictors column. Note length pred.labels must exactly match amount predictors Predictor column. dv.labels change names model columns, labelled variable labels / names dependent variables. , various string.*-arguments, change name column headings.","code":"tab_model(   m1, m2,    pred.labels = c(\"Intercept\", \"Age (Carer)\", \"Hours per Week\", \"Gender (Carer)\",                   \"Education: middle (Carer)\", \"Education: high (Carer)\",                    \"Age (Older Person)\"),   dv.labels = c(\"First Model\", \"M2\"),   string.pred = \"Coeffcient\",   string.ci = \"Conf. Int (95%)\",   string.p = \"P-Value\" )"},{"path":"https://strengejacke.github.io/sjPlot/articles/tab_model_estimates.html","id":"including-reference-level-of-categorical-predictors","dir":"Articles","previous_headings":"","what":"Including reference level of categorical predictors","title":"Summary of Regression Models as HTML Table","text":"default, categorical predictors, variable names categories regression coefficients shown table output. can include reference level categorical predictors setting show.reflvl = TRUE. show variable names, categories include reference level, also set prefix.labels = \"varname\".","code":"library(glmmTMB) data(\"Salamanders\") model <- glm(   count ~ spp + Wtemp + mined + cover,   family = poisson(),   data = Salamanders )  tab_model(model) tab_model(model, show.reflvl = TRUE) tab_model(model, show.reflvl = TRUE, prefix.labels = \"varname\")"},{"path":"https://strengejacke.github.io/sjPlot/articles/tab_model_estimates.html","id":"style-of-p-values","dir":"Articles","previous_headings":"","what":"Style of p-values","title":"Summary of Regression Models as HTML Table","text":"can change style p-values displayed argument p.style. p.style = \"stars\", p-values indicated * table. p<0.05   ** p<0.01   *** p<0.001 Another option scientific notation, using p.style = \"scientific\", also can combined digits.p.","code":"tab_model(m1, m2, p.style = \"stars\") tab_model(m1, m2, p.style = \"scientific\", digits.p = 2)"},{"path":"https://strengejacke.github.io/sjPlot/articles/tab_model_estimates.html","id":"automatic-matching-for-named-vectors","dir":"Articles","previous_headings":"Style of p-values","what":"Automatic matching for named vectors","title":"Summary of Regression Models as HTML Table","text":"Another way easily assign labels named vectors. case, doesn’t matter pred.labels labels coefficients model(s), order labels passed tab_model(). requirement labels’ names equal coefficients names appear summary()-output.","code":"# example, coefficients are \"c161sex2\" or \"c172code3\" summary(m1) #>  #> Call: #> lm(formula = barthtot ~ c160age + c12hour + c161sex + c172code,  #>     data = efc) #>  #> Residuals: #>     Min      1Q  Median      3Q     Max  #> -75.144 -14.944   4.401  18.661  72.393  #>  #> Coefficients: #>             Estimate Std. Error t value Pr(>|t|)     #> (Intercept) 87.14994    4.68009  18.621  < 2e-16 *** #> c160age     -0.20716    0.07211  -2.873  0.00418 **  #> c12hour     -0.27883    0.01865 -14.950  < 2e-16 *** #> c161sex2    -0.39402    2.08893  -0.189  0.85044     #> c172code2    1.36596    2.28440   0.598  0.55004     #> c172code3   -1.64045    2.84037  -0.578  0.56373     #> --- #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #>  #> Residual standard error: 25.35 on 815 degrees of freedom #>   (87 observations deleted due to missingness) #> Multiple R-squared:  0.2708, Adjusted R-squared:  0.2664  #> F-statistic: 60.54 on 5 and 815 DF,  p-value: < 2.2e-16  pl <- c(   `(Intercept)` = \"Intercept\",   e17age = \"Age (Older Person)\",   c160age = \"Age (Carer)\",    c12hour = \"Hours per Week\",    barthtot = \"Barthel-Index\",   c161sex2 = \"Gender (Carer)\",   c172code2 = \"Education: middle (Carer)\",    c172code3 = \"Education: high (Carer)\",   a_non_used_label = \"We don't care\" )   tab_model(   m1, m2, m3, m4,    pred.labels = pl,    dv.labels = c(\"Model1\", \"Model2\", \"Model3\", \"Model4\"),   show.ci = FALSE,    show.p = FALSE,    transform = NULL )"},{"path":"https://strengejacke.github.io/sjPlot/articles/tab_model_estimates.html","id":"keep-or-remove-coefficients-from-the-table","dir":"Articles","previous_headings":"","what":"Keep or remove coefficients from the table","title":"Summary of Regression Models as HTML Table","text":"Using terms- rm.terms-argument allows us explicitly show remove specific coefficients table output. Note names terms keep remove match coefficients names. categorical predictors, one example :","code":"tab_model(m1, terms = c(\"c160age\", \"c12hour\")) tab_model(m1, rm.terms = c(\"c172code2\", \"c161sex2\"))"},{"path":[]},{"path":"https://strengejacke.github.io/sjPlot/articles/tab_model_robust.html","id":"robust-covariance-matrix-estimation-from-model-parameters","dir":"Articles","previous_headings":"Classical Regression Models","what":"Robust Covariance Matrix Estimation from Model Parameters","title":"Robust Estimation of Standard Errors, Confidence Intervals and p-values","text":"two arguments allow choosing different methods options robust estimation: vcov.fun vcov.args. Let us start simple example, uses heteroskedasticity-consistent covariance matrix estimation estimation-type “HC3” (.e. sandwich::vcovHC(type = \"HC3\") called):","code":"data(iris) model <- lm(Petal.Length ~ Sepal.Length * Species + Sepal.Width, data = iris)  # model parameters, where SE, CI and p-values are based on robust estimation tab_model(model, vcov.fun = \"HC3\", show.se = TRUE) # compare standard errors to result from sandwich-package unname(sqrt(diag(sandwich::vcovHC(model)))) #> [1] 0.45382603 0.11884474 0.69296611 0.63031982 0.08318559 0.13045539 0.11841325"},{"path":"https://strengejacke.github.io/sjPlot/articles/tab_model_robust.html","id":"cluster-robust-covariance-matrix-estimation-sandwich","dir":"Articles","previous_headings":"Classical Regression Models","what":"Cluster-Robust Covariance Matrix Estimation (sandwich)","title":"Robust Estimation of Standard Errors, Confidence Intervals and p-values","text":"another covariance matrix estimation required, use vcov.fun-argument. argument needs suffix related vcov*()-functions value, .e. vcov.fun = \"CL\" call sandwich::vcovCL(), vcov.fun = \"HAC\" call sandwich::vcovHAC(). specific estimation type can changed vcov.args. E.g., sandwich::vcovCL() accepts estimation types HC0 HC3. next example, use clustered covariance matrix estimation HC1-estimation type. Usually, clustered covariance matrix estimation used cluster-structure data. variable indicating cluster-structure can defined sandwich::vcovCL() cluster-argument. tab_model(), additional arguments passed functions sandwich package can specified vcov.args:","code":"# change estimation-type tab_model(model, vcov.fun = \"CL\", vcov.args = list(type = \"HC1\"), show.se = TRUE) # compare standard errors to result from sandwich-package unname(sqrt(diag(sandwich::vcovCL(model)))) #> [1] 0.42197635 0.11148130 0.65274212 0.58720711 0.07934029 0.12251570 0.11058144 iris$cluster <- factor(rep(LETTERS[1:8], length.out = nrow(iris))) # change estimation-type, defining additional arguments tab_model(   model,    vcov.fun = \"CL\",    vcov.args = list(type = \"HC1\", cluster = iris$cluster),   show.se = TRUE ) # compare standard errors to result from sandwich-package unname(sqrt(diag(sandwich::vcovCL(model, cluster = iris$cluster)))) #> [1] 0.33714287 0.07192334 0.51893777 0.26415406 0.07201145 0.09661348 0.05123446"},{"path":"https://strengejacke.github.io/sjPlot/articles/tab_model_robust.html","id":"cluster-robust-covariance-matrix-estimation-clubsandwich","dir":"Articles","previous_headings":"Classical Regression Models","what":"Cluster-Robust Covariance Matrix Estimation (clubSandwich)","title":"Robust Estimation of Standard Errors, Confidence Intervals and p-values","text":"Cluster-robust estimation variance-covariance matrix can also achieved using clubSandwich::vcovCR(). Thus, vcov.fun = \"CR\", related function clubSandwich package called. Note function requires specification cluster-argument.","code":"# create fake-cluster-variable, to demonstrate cluster robust standard errors iris$cluster <- factor(rep(LETTERS[1:8], length.out = nrow(iris)))  # cluster-robust estimation tab_model(   model,    vcov.fun = \"CR1\",    vcov.args = list(cluster = iris$cluster),   show.se = TRUE ) # compare standard errors to result from clubSsandwich-package unname(sqrt(diag(clubSandwich::vcovCR(model, type = \"CR1\", cluster = iris$cluster)))) #> [1] 0.33028501 0.07046034 0.50838200 0.25878087 0.07054666 0.09464825 0.05019229"},{"path":"https://strengejacke.github.io/sjPlot/articles/tab_model_robust.html","id":"robust-covariance-matrix-estimation-on-standardized-model-parameters","dir":"Articles","previous_headings":"Classical Regression Models","what":"Robust Covariance Matrix Estimation on Standardized Model Parameters","title":"Robust Estimation of Standard Errors, Confidence Intervals and p-values","text":"Finally, robust estimation can combined standardization. However, robust covariance matrix estimation works show.std = \"std\".","code":"# model parameters, robust estimation on standardized model tab_model(   model,    show.std = \"std\",   vcov.fun = \"HC\" )"},{"path":[]},{"path":"https://strengejacke.github.io/sjPlot/articles/tab_model_robust.html","id":"robust-covariance-matrix-estimation-for-mixed-models","dir":"Articles","previous_headings":"Mixed Models","what":"Robust Covariance Matrix Estimation for Mixed Models","title":"Robust Estimation of Standard Errors, Confidence Intervals and p-values","text":"linear mixed models, definition clustered (“hierarchical” multilevel) structure data, also possible estimate cluster-robust covariance matrix. possible due clubSandwich package, thus need define arguments example.","code":"library(lme4) data(iris) set.seed(1234) iris$grp <- as.factor(sample(1:3, nrow(iris), replace = TRUE))  # fit example model model <- lme4::lmer(   Sepal.Length ~ Species * Sepal.Width + Petal.Length + (1 | grp),   data = iris )  # normal model parameters, like from 'summary()' tab_model(model) # model parameters, cluster robust estimation for mixed models tab_model(   model,    vcov.fun = \"CR1\",    vcov.args = list(cluster = iris$grp) )"},{"path":"https://strengejacke.github.io/sjPlot/articles/tab_model_robust.html","id":"robust-covariance-matrix-estimation-on-standardized-mixed-model-parameters","dir":"Articles","previous_headings":"Mixed Models","what":"Robust Covariance Matrix Estimation on Standardized Mixed Model Parameters","title":"Robust Estimation of Standard Errors, Confidence Intervals and p-values","text":", robust estimation can combined standardization linear mixed models well, cases also works show.std = \"std\".","code":"# model parameters, cluster robust estimation on standardized mixed model tab_model(   model,    show.std = \"std\",   vcov.fun = \"CR1\",    vcov.args = list(cluster = iris$grp) )"},{"path":[]},{"path":"https://strengejacke.github.io/sjPlot/articles/table_css.html","id":"export-table-as-html-file-to-open-in-word-processors","dir":"Articles","previous_headings":"Copying table output to office or word processors","what":"Export table as HTML file to open in word processors","title":"Customizing HTML tables","text":"can save HTML page file usage specifying file-argument saved HTML file can opened word processors like LibreOffice Microsoft Office.","code":""},{"path":"https://strengejacke.github.io/sjPlot/articles/table_css.html","id":"drag-and-drop-from-browser-or-rstudio-viewer-pane","dir":"Articles","previous_headings":"Copying table output to office or word processors","what":"Drag and drop from browser or RStudio viewer pane","title":"Customizing HTML tables","text":"can directly drag drop table RStudio viewer pane browser word processor. Simply select complete table mouse drag office.","code":""},{"path":"https://strengejacke.github.io/sjPlot/articles/table_css.html","id":"customizing-table-output-with-the-css-parameter","dir":"Articles","previous_headings":"","what":"Customizing table output with the CSS parameter","title":"Customizing HTML tables","text":"table output HTML format. table style (visual appearance) formatted using Cascading Style Sheets (CSS). bit familiar topics, can easily customize appearance table output. Many table elements (header, row, column, cell, summary row, first row column…) CSS-class attributes, can used change table style. Since sjt.* function well tab_model() different table elements thus different class attributes, first need know styles can customized.","code":""},{"path":"https://strengejacke.github.io/sjPlot/articles/table_css.html","id":"retrieving-customizable-styles","dir":"Articles","previous_headings":"Customizing table output with the CSS parameter","what":"Retrieving customizable styles","title":"Customizing HTML tables","text":"table functions invisibly return several values. return value page.style contains style information HTML table. can print style sheet console using cat()-function: HTML code page.content return value. following code prints HTML code table R console: Now can see table elements associated CSS class attributes.","code":"library(sjPlot) data(efc) m <- lm(barthtot ~ c160age + c12hour + c161sex + c172code, data = efc) tab <- tab_model(m) cat(tab$page.style) #> <style> #> html, body { background-color: white; } #> table { border-collapse:collapse; border:none; } #> caption { font-weight: bold; text-align:left; } #> td {  } #> .thead { border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; } #> .tdata { padding:0.2cm; text-align:left; vertical-align:top; } #> .arc { background-color:#f2f2f2; } #> .summary { padding-top:0.1cm; padding-bottom:0.1cm; } #> .summarydata { text-align:left; } #> .fixedparts { font-weight:bold; text-align:left; } #> .randomparts { font-weight:bold; text-align:left; padding-top:.8em; } #> .zeroparts { font-weight:bold; text-align:left; padding-top:.8em; } #> .simplexparts { font-weight:bold; text-align:left; padding-top:.8em; } #> .lasttablerow { border-bottom: double; } #> .firsttablerow {  } #> .firstsumrow { border-top:1px solid; } #> .labelcellborder { border-bottom:1px solid; } #> .depvarhead { text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal; } #> .depvarheadnodv { border-top: double; text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal; } #> .leftalign { text-align:left; } #> .centeralign { text-align:center; } #> .firsttablecol { text-align:left; } #> .footnote { font-style:italic; border-top:double black; text-align:right; } #> .subtitle { font-weight: normal; } #> .modelcolumn1 {  } #> .modelcolumn2 {  } #> .modelcolumn3 {  } #> .modelcolumn4 {  } #> .modelcolumn5 {  } #> .modelcolumn6 {  } #> .modelcolumn7 {  } #> .col1 {  } #> .col2 {  } #> .col3 {  } #> .col4 {  } #> .col5 {  } #> .col6 {  } #> <\/style> cat(tab$page.content) #> <table> #> <tr> #> <th class=\"thead firsttablerow firsttablecol col1\">&nbsp;<\/th> #> <th colspan=\"3\" class=\"thead firsttablerow\">Total score BARTHEL INDEX<\/th> #> <\/tr> #> <tr> #> <td class=\"depvarhead firsttablerow firsttablecol col1\">Predictors<\/td> #> <td class=\"depvarhead firsttablerow col2\">Estimates<\/td> #> <td class=\"depvarhead firsttablerow col3\">CI<\/td> #> <td class=\"depvarhead firsttablerow col4\">p<\/td> #> <\/tr> #> <tr> #> <td class=\"tdata firsttablecol col1\">(Intercept)<\/td> #> <td class=\"tdata centeralign modelcolumn1 col2\">90.06<\/td> #> <td class=\"tdata centeralign modelcolumn1 col3\">77.95&nbsp;&ndash;&nbsp;102.18<\/td> #> <td class=\"tdata centeralign modelcolumn1 col4\"><strong>&lt;0.001<\/strong><\/td> #> <\/tr> #> <tr> #> <td class=\"tdata firsttablecol col1\">carer' age<\/td> #> <td class=\"tdata centeralign modelcolumn1 col2\">&#45;0.22<\/td> #> <td class=\"tdata centeralign modelcolumn1 col3\">&#45;0.36&nbsp;&ndash;&nbsp;-0.08<\/td> #> <td class=\"tdata centeralign modelcolumn1 col4\"><strong>0.002<\/strong><\/td> #> <\/tr> #> <tr> #> <td class=\"tdata firsttablecol col1\">average number of hours<br>of care per week<\/td> #> <td class=\"tdata centeralign modelcolumn1 col2\">&#45;0.28<\/td> #> <td class=\"tdata centeralign modelcolumn1 col3\">&#45;0.31&nbsp;&ndash;&nbsp;-0.24<\/td> #> <td class=\"tdata centeralign modelcolumn1 col4\"><strong>&lt;0.001<\/strong><\/td> #> <\/tr> #> <tr> #> <td class=\"tdata firsttablecol col1\">carer's gender<\/td> #> <td class=\"tdata centeralign modelcolumn1 col2\">&#45;0.26<\/td> #> <td class=\"tdata centeralign modelcolumn1 col3\">&#45;4.36&nbsp;&ndash;&nbsp;3.83<\/td> #> <td class=\"tdata centeralign modelcolumn1 col4\">0.900<\/td> #> <\/tr> #> <tr> #> <td class=\"tdata firsttablecol col1\">carer's level of<br>education<\/td> #> <td class=\"tdata centeralign modelcolumn1 col2\">&#45;0.76<\/td> #> <td class=\"tdata centeralign modelcolumn1 col3\">&#45;3.55&nbsp;&ndash;&nbsp;2.02<\/td> #> <td class=\"tdata centeralign modelcolumn1 col4\">0.592<\/td> #> <\/tr> #> <tr> #> <td class=\"tdata leftalign summary firstsumrow\">Observations<\/td> #> <td class=\"tdata summary summarydata firstsumrow\" colspan=\"3\">821<\/td> #> <\/tr> #> <tr> #> <td class=\"tdata leftalign summary\">R<sup>2<\/sup> / R<sup>2<\/sup> adjusted<\/td> #> <td class=\"tdata summary summarydata\" colspan=\"3\">0.270 / 0.266<\/td> #> <\/tr> #>  #> <\/table>"},{"path":"https://strengejacke.github.io/sjPlot/articles/table_css.html","id":"customizing-table-output-with-the-css-parameter-1","dir":"Articles","previous_headings":"","what":"Customizing table output with the CSS parameter","title":"Customizing HTML tables","text":"can customize table output CSS parameter. parameter requires list attributes, follow certain pattern: attributes needs css. prefix followed class name (e.g. caption, thead, centeralign, etc.) equal-sign CSS format ((single) quotation marks) CSS format must end colon (;) Example: example, header row lost original style just became red. want keep original style just add additional style information, use plus-sign (+) initial character parameter attributes. following example, header row keeps original style additionally printed red:","code":"tab_model(   m,   CSS = list(     css.depvarhead = 'color: red;',     css.centeralign = 'text-align: left;',      css.firsttablecol = 'font-weight: bold;',      css.summary = 'color: blue;'   ) ) tab_model(m, CSS = list(css.depvarhead = '+color: red;'))"},{"path":"https://strengejacke.github.io/sjPlot/articles/table_css.html","id":"pre-defined-table-layouts","dir":"Articles","previous_headings":"","what":"Pre-defined Table-Layouts","title":"Customizing HTML tables","text":"pre-defined CSS-themes, can accessed css_theme()-function. pre-defined themes planned future.","code":"tab_model(m, CSS = css_theme(\"cells\"))"},{"path":"https://strengejacke.github.io/sjPlot/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Daniel Lüdecke. Author, maintainer. Alexander Bartel. Contributor. Carsten Schwemmer. Contributor. Chuck Powell. Contributor. Amir Djalovski. Contributor. Johannes Titz. Contributor.","code":""},{"path":"https://strengejacke.github.io/sjPlot/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Lüdecke D (????). sjPlot: Data Visualization Statistics Social Science. R package version 2.8.16.3, https://CRAN.R-project.org/package=sjPlot.","code":"@Manual{,   title = {sjPlot: Data Visualization for Statistics in Social Science},   author = {Daniel Lüdecke},   note = {R package version 2.8.16.3},   url = {https://CRAN.R-project.org/package=sjPlot}, }"},{"path":"https://strengejacke.github.io/sjPlot/index.html","id":"sjplot---data-visualization-for-statistics-in-social-science-","dir":"","previous_headings":"","what":"Data Visualization for Statistics in Social Science","title":"Data Visualization for Statistics in Social Science","text":"Collection plotting table output functions data visualization. Results various statistical analyses (commonly used social sciences) can visualized using package, including simple cross tabulated frequencies, histograms, box plots, (generalized) linear models, mixed effects models, PCA correlation matrices, cluster analyses, scatter plots, Likert scales, effects plots interaction terms regression models, constructing index score variables much .","code":""},{"path":[]},{"path":"https://strengejacke.github.io/sjPlot/index.html","id":"latest-development-build","dir":"","previous_headings":"Installation","what":"Latest development build","title":"Data Visualization for Statistics in Social Science","text":"install latest development snapshot (see latest changes ), type following commands R console:","code":"library(devtools) devtools::install_github(\"strengejacke/sjPlot\")"},{"path":"https://strengejacke.github.io/sjPlot/index.html","id":"official-stable-release","dir":"","previous_headings":"Installation","what":"Official, stable release","title":"Data Visualization for Statistics in Social Science","text":"install latest stable release CRAN, type following command R console:","code":"install.packages(\"sjPlot\")"},{"path":"https://strengejacke.github.io/sjPlot/index.html","id":"documentation-and-examples","dir":"","previous_headings":"","what":"Documentation and examples","title":"Data Visualization for Statistics in Social Science","text":"Please visit https://strengejacke.github.io/sjPlot/ documentation vignettes.","code":""},{"path":"https://strengejacke.github.io/sjPlot/index.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Data Visualization for Statistics in Social Science","text":"case want / cite package, please use citation('sjPlot') citation information. Since core functionality package depends ggplot-package, consider citing package well.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/dist_chisq.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot chi-squared distributions — dist_chisq","title":"Plot chi-squared distributions — dist_chisq","text":"function plots simple chi-squared distribution chi-squared distribution                shaded areas indicate chi-squared value significant p-level                reached.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/dist_chisq.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot chi-squared distributions — dist_chisq","text":"","code":"dist_chisq(   chi2 = NULL,   deg.f = NULL,   p = NULL,   xmax = NULL,   geom.colors = NULL,   geom.alpha = 0.7 )"},{"path":"https://strengejacke.github.io/sjPlot/reference/dist_chisq.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot chi-squared distributions — dist_chisq","text":"chi2 Numeric, optional. specified, chi-squared distribution deg.f degrees freedom plotted shaded area chi2 value position plotted indicates whether specified value significant . chi2 p specified, distribution without shaded area plotted. deg.f Numeric. degrees freedom chi-squared distribution. Needs specified. p Numeric, optional. specified, chi-squared distribution deg.f degrees freedom plotted shaded area position specified p-level starts plotted. chi2 p specified, distribution without shaded area plotted. xmax Numeric, optional. Specifies maximum x-axis-value. specified, x-axis ranges value p-level 0.00001 reached. geom.colors user defined color geoms. See 'Details' plot_grpfrq. geom.alpha Specifies alpha-level shaded area. Default 0.7, range 0 1.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/dist_chisq.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot chi-squared distributions — dist_chisq","text":"","code":"# a simple chi-squared distribution # for 6 degrees of freedom dist_chisq(deg.f = 6)   # a chi-squared distribution for 6 degrees of freedom, # and a shaded area starting at chi-squared value of ten. # With a df of 6, a chi-squared value of 12.59 would be \"significant\", # thus the shaded area from 10 to 12.58 is filled as \"non-significant\", # while the area starting from chi-squared value 12.59 is filled as # \"significant\" dist_chisq(chi2 = 10, deg.f = 6)   # a chi-squared distribution for 6 degrees of freedom, # and a shaded area starting at that chi-squared value, which has # a p-level of about 0.125 (which equals a chi-squared value of about 10). # With a df of 6, a chi-squared value of 12.59 would be \"significant\", # thus the shaded area from 10 to 12.58 (p-level 0.125 to p-level 0.05) # is filled as \"non-significant\", while the area starting from chi-squared # value 12.59 (p-level < 0.05) is filled as \"significant\". dist_chisq(p = 0.125, deg.f = 6)"},{"path":"https://strengejacke.github.io/sjPlot/reference/dist_f.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot F distributions — dist_f","title":"Plot F distributions — dist_f","text":"function plots simple F distribution F distribution                shaded areas indicate F value significant p-level                reached.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/dist_f.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot F distributions — dist_f","text":"","code":"dist_f(   f = NULL,   deg.f1 = NULL,   deg.f2 = NULL,   p = NULL,   xmax = NULL,   geom.colors = NULL,   geom.alpha = 0.7 )"},{"path":"https://strengejacke.github.io/sjPlot/reference/dist_f.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot F distributions — dist_f","text":"f Numeric, optional. specified, F distribution deg.f1 deg.f2 degrees freedom plotted shaded area f value position plotted indicates whether specified value significant . f p specified, distribution without shaded area plotted. deg.f1 Numeric. first degrees freedom F distribution. Needs specified. deg.f2 Numeric. second degrees freedom F distribution. Needs specified. p Numeric, optional. specified, F distribution deg.f1 deg.f2 degrees freedom plotted shaded area position specified p-level starts plotted. f p specified, distribution without shaded area plotted. xmax Numeric, optional. Specifies maximum x-axis-value. specified, x-axis ranges value p-level 0.00001 reached. geom.colors user defined color geoms. See 'Details' plot_grpfrq. geom.alpha Specifies alpha-level shaded area. Default 0.7, range 0 1.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/dist_f.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot F distributions — dist_f","text":"","code":"# a simple F distribution for 6 and 45 degrees of freedom dist_f(deg.f1 = 6, deg.f2 = 45)   # F distribution for 6 and 45 degrees of freedom, # and a shaded area starting at F value of two. # F-values equal or greater than 2.31 are \"significant\" dist_f(f = 2, deg.f1 = 6, deg.f2 = 45)   # F distribution for 6 and 45 degrees of freedom, # and a shaded area starting at a p-level of 0.2 # (F-Value about 1.5). dist_f(p = 0.2, deg.f1 = 6, deg.f2 = 45)"},{"path":"https://strengejacke.github.io/sjPlot/reference/dist_norm.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot normal distributions — dist_norm","title":"Plot normal distributions — dist_norm","text":"function plots simple normal distribution normal distribution                shaded areas indicate value significant p-level                reached.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/dist_norm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot normal distributions — dist_norm","text":"","code":"dist_norm(   norm = NULL,   mean = 0,   sd = 1,   p = NULL,   xmax = NULL,   geom.colors = NULL,   geom.alpha = 0.7 )"},{"path":"https://strengejacke.github.io/sjPlot/reference/dist_norm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot normal distributions — dist_norm","text":"norm Numeric, optional. specified, normal distribution mean sd plotted shaded area norm value position plotted indicates whether specified value significant . norm p specified, distribution without shaded area plotted. mean Numeric. Mean value normal distribution. default 0. sd Numeric. Standard deviation normal distribution. default 1. p Numeric, optional. specified, normal distribution mean sd plotted shaded area position specified p-level starts plotted. norm p specified, distribution without shaded area plotted. xmax Numeric, optional. Specifies maximum x-axis-value. specified, x-axis ranges value p-level 0.00001 reached. geom.colors user defined color geoms. See 'Details' plot_grpfrq. geom.alpha Specifies alpha-level shaded area. Default 0.7, range 0 1.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/dist_norm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot normal distributions — dist_norm","text":"","code":"# a simple normal distribution dist_norm()   # a simple normal distribution with different mean and sd. # note that curve looks similar to above plot, but axis range # has changed. dist_norm(mean = 2, sd = 4)   # a simple normal distribution dist_norm(norm = 1)   # a simple normal distribution dist_norm(p = 0.2)"},{"path":"https://strengejacke.github.io/sjPlot/reference/dist_t.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot t-distributions — dist_t","title":"Plot t-distributions — dist_t","text":"function plots simple t-distribution t-distribution                shaded areas indicate t-value significant p-level                reached.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/dist_t.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot t-distributions — dist_t","text":"","code":"dist_t(   t = NULL,   deg.f = NULL,   p = NULL,   xmax = NULL,   geom.colors = NULL,   geom.alpha = 0.7 )"},{"path":"https://strengejacke.github.io/sjPlot/reference/dist_t.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot t-distributions — dist_t","text":"t Numeric, optional. specified, t-distribution deg.f degrees freedom plotted shaded area t value position plotted indicates whether specified value significant . t p specified, distribution without shaded area plotted. deg.f Numeric. degrees freedom t-distribution. Needs specified. p Numeric, optional. specified, t-distribution deg.f degrees freedom plotted shaded area position specified p-level starts plotted. t p specified, distribution without shaded area plotted. xmax Numeric, optional. Specifies maximum x-axis-value. specified, x-axis ranges value p-level 0.00001 reached. geom.colors user defined color geoms. See 'Details' plot_grpfrq. geom.alpha Specifies alpha-level shaded area. Default 0.7, range 0 1.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/dist_t.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot t-distributions — dist_t","text":"","code":"# a simple t-distribution # for 6 degrees of freedom dist_t(deg.f = 6)   # a t-distribution for 6 degrees of freedom, # and a shaded area starting at t-value of one. # With a df of 6, a t-value of 1.94 would be \"significant\". dist_t(t = 1, deg.f = 6)   # a t-distribution for 6 degrees of freedom, # and a shaded area starting at p-level of 0.4 # (t-value of about 0.26). dist_t(p = 0.4, deg.f = 6)"},{"path":"https://strengejacke.github.io/sjPlot/reference/efc.html","id":null,"dir":"Reference","previous_headings":"","what":"Sample dataset from the EUROFAMCARE project — efc","title":"Sample dataset from the EUROFAMCARE project — efc","text":"SPSS sample data set, imported read_spss function.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_frq.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot frequencies of variables — plot_frq","title":"Plot frequencies of variables — plot_frq","text":"Plot frequencies variable bar graph, histogram, box plot etc.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_frq.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot frequencies of variables — plot_frq","text":"","code":"plot_frq(   data,   ...,   title = \"\",   weight.by = NULL,   title.wtd.suffix = NULL,   sort.frq = c(\"none\", \"asc\", \"desc\"),   type = c(\"bar\", \"dot\", \"histogram\", \"line\", \"density\", \"boxplot\", \"violin\"),   geom.size = NULL,   geom.colors = \"#336699\",   errorbar.color = \"darkred\",   axis.title = NULL,   axis.labels = NULL,   xlim = NULL,   ylim = NULL,   wrap.title = 50,   wrap.labels = 20,   grid.breaks = NULL,   expand.grid = FALSE,   show.values = TRUE,   show.n = TRUE,   show.prc = TRUE,   show.axis.values = TRUE,   show.ci = FALSE,   show.na = FALSE,   show.mean = FALSE,   show.mean.val = TRUE,   show.sd = TRUE,   drop.empty = TRUE,   mean.line.type = 2,   mean.line.size = 0.5,   inner.box.width = 0.15,   inner.box.dotsize = 3,   normal.curve = FALSE,   normal.curve.color = \"red\",   normal.curve.size = 0.8,   normal.curve.alpha = 0.4,   auto.group = NULL,   coord.flip = FALSE,   vjust = \"bottom\",   hjust = \"center\",   y.offset = NULL )"},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_frq.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot frequencies of variables — plot_frq","text":"data data frame, grouped data frame. ... Optional, unquoted names variables selected processing. Required, data data frame (vector) selected variables data processed. may also use functions like : tidyselect's select_helpers. title Character vector, used plot title. default, response_labels called retrieve label dependent variable, used title. Use title = \"\" remove title. weight.Vector weights applied weight cases. Must vector length input vector. Default NULL, weights used. title.wtd.suffix Suffix (string) title, weight.specified, e.g. title.wtd.suffix=\" (weighted)\". Default NULL, title suffix cases weighted. sort.frq Determines whether categories sorted according frequencies . Default \"none\", categories sorted frequency. Use \"asc\" \"desc\" sorting categories ascending descending order. type Specifies plot type. May abbreviated. \"bar\" simple bars (default) \"dot\" dot plot \"histogram\" histogram (apply grouped frequencies) \"line\" line-styled histogram filled area \"density\" density plot (apply grouped frequencies) \"boxplot\" box plot \"violin\" violin plots geom.size size resp. width geoms (bar width, line thickness point size, depending plot type function). Note bar bin widths mostly need smaller values dot sizes. geom.colors User defined color geoms, e.g. geom.colors = \"#0080ff\". errorbar.color Color confidence interval bars (error bars). applies type = \"bar\". case dot plots, error bars colors dots (see geom.colors). axis.title Character vector length one two (depending plot function type), used title(s) x y axis. specified, default labelling  chosen. Note: plot types support argument. cases, use return value add axis titles manually labs, e.g.: $plot.list[[1]] + labs(x = ...) axis.labels character vector labels used axis labels. Optional argument, since cases, axis labels set automatically. xlim Numeric vector length two, defining lower upper axis limits x scale. default, argument set NULL, .e. x-axis fits required range data. ylim numeric vector length two, defining lower upper axis limits y scale. default, argument set NULL, .e. y-axis fits required range data. wrap.title Numeric, determines many chars plot title displayed one line line break inserted. wrap.labels numeric, determines many chars value, variable axis labels displayed one line line break inserted. grid.breaks numeric; sets distance breaks axis, .e. every grid.breaks'th position major grid printed. expand.grid logical, TRUE, plot grid expanded, .e. small margin axes plotting region. Default FALSE. show.values Logical, whether values plotted . show.n logical, TRUE, adds total number cases group category labels. show.prc logical, TRUE (default), percentage values plotted bar FALSE, percentage values removed. show.axis.values logical, whether category, count percentage values axis printed . show.ci Logical, TRUE), adds notches box plot, used compare groups; notches two boxes overlap, medians considered significantly different. show.na logical, TRUE, NA's (missing values) added output. show.mean Logical, TRUE, vertical line histograms drawn indicate mean value variables. applies histogram-charts. show.mean.val Logical, TRUE (default), mean value printed vertical line indicates variable's mean. applies histogram-charts. show.sd Logical, TRUE, standard deviation annotated shaded rectangle around mean intercept line. applies histogram-charts. drop.empty Logical, TRUE variable's values labeled, values / factor levels occurrence data omitted output. FALSE, labeled values observations still printed table (frequency 0). mean.line.type Numeric value, indicating linetype mean intercept line. applies histogram-charts show.mean = TRUE. mean.line.size Numeric, size mean intercept line. applies histogram-charts show.mean = TRUE. inner.box.width width inner box plot plotted inside violin plots. applies type = \"violin\". Default value 0.15 inner.box.dotsize size mean dot insie violin box plot. Applies type = \"violin\" \"boxplot\". normal.curve Logical, TRUE, normal curve, adjusted data, plotted histogram density plot. Default FALSE. applies histograms density plots plotted (see type). normal.curve.color Color normal curve line. applies normal.curve = TRUE. normal.curve.size Numeric, size normal curve line. applies normal.curve = TRUE. normal.curve.alpha Transparancy level (alpha value) normal curve. applies normal.curve = TRUE. auto.group numeric value, indicating minimum amount unique values count variable, automatic grouping smaller units done (see group_var). Default value auto.group NULL, .e. auto-grouping . See group_var examples grouping. coord.flip logical, TRUE, x y axis swapped. vjust character vector, indicating vertical position value labels. Allowed values vjust aesthetics ggplot2: \"left\", \"center\", \"right\", \"bottom\", \"middle\", \"top\" new options like \"inward\" \"outward\", align text towards away center plot respectively. hjust character vector, indicating horizontal position value labels. Allowed values vjust aesthetics ggplot2: \"left\", \"center\", \"right\", \"bottom\", \"middle\", \"top\" new options like \"inward\" \"outward\", align text towards away center plot respectively. y.offset numeric, offset text labels alignment adjusted top/bottom geom (see hjust vjust).","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_frq.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot frequencies of variables — plot_frq","text":"ggplot-object.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_frq.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Plot frequencies of variables — plot_frq","text":"function works variables integer values (numeric         factor levels), .e. scales / centered variables         fractional part may result unexpected behaviour.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_frq.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot frequencies of variables — plot_frq","text":"","code":"library(sjlabelled) data(efc) data(iris)  # simple plots, two different notations plot_frq(iris, Species)  plot_frq(efc$tot_sc_e)   # boxplot plot_frq(efc$e17age, type = \"box\")   if (require(\"dplyr\")) {   # histogram, pipe-workflow   efc %>%     dplyr::select(e17age, c160age) %>%     plot_frq(type = \"hist\", show.mean = TRUE)    # bar plot(s)   plot_frq(efc, e42dep, c172code) } #> Loading required package: dplyr #>  #> Attaching package: ‘dplyr’ #> The following object is masked from ‘package:sjlabelled’: #>  #>     as_label #> The following objects are masked from ‘package:stats’: #>  #>     filter, lag #> The following objects are masked from ‘package:base’: #>  #>     intersect, setdiff, setequal, union #> [[1]]  #>  #> [[2]]  #>   if (require(\"dplyr\") && require(\"gridExtra\")) {   # grouped data frame, all panels in one plot   efc %>%     group_by(e42dep) %>%     plot_frq(c161sex) %>%     plot_grid() } #> Loading required package: gridExtra #>  #> Attaching package: ‘gridExtra’ #> The following object is masked from ‘package:dplyr’: #>  #>     combine   # \\donttest{ library(sjmisc) # grouped variable ageGrp <- group_var(efc$e17age) ageGrpLab <- group_labels(efc$e17age) plot_frq(ageGrp, title = get_label(efc$e17age), axis.labels = ageGrpLab)   # plotting confidence intervals. expand grid and v/hjust for text labels plot_frq(   efc$e15relat, type = \"dot\", show.ci = TRUE, sort.frq = \"desc\",   coord.flip = TRUE, expand.grid = TRUE, vjust = \"bottom\", hjust = \"left\" )   # histogram with overlayed normal curve plot_frq(efc$c160age, type = \"h\", show.mean = TRUE, show.mean.val = TRUE,         normal.curve = TRUE, show.sd = TRUE, normal.curve.color = \"blue\",         normal.curve.size = 3, ylim = c(0,50)) #> Warning: Removed 4 rows containing missing values or values outside the scale range #> (`geom_bar()`).  # }"},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_gpt.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot grouped proportional tables — plot_gpt","title":"Plot grouped proportional tables — plot_gpt","text":"Plot grouped proportional crosstables, proportion                level x highest category y                plotted, subgroup grp.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_gpt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot grouped proportional tables — plot_gpt","text":"","code":"plot_gpt(   data,   x,   y,   grp,   colors = \"metro\",   geom.size = 2.5,   shape.fill.color = \"#f0f0f0\",   shapes = c(15, 16, 17, 18, 21, 22, 23, 24, 25, 7, 8, 9, 10, 12),   title = NULL,   axis.labels = NULL,   axis.titles = NULL,   legend.title = NULL,   legend.labels = NULL,   wrap.title = 50,   wrap.labels = 15,   wrap.legend.title = 20,   wrap.legend.labels = 20,   axis.lim = NULL,   grid.breaks = NULL,   show.total = TRUE,   annotate.total = TRUE,   show.p = TRUE,   show.n = TRUE )"},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_gpt.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot grouped proportional tables — plot_gpt","text":"data data frame, grouped data frame. x Categorical variable, proportion category x highest category y printed along x-axis. y Categorical numeric variable. binary variable, y recoded binary variable, dichtomized highest category remaining categories. grp Grouping variable, define y-axis colors May character vector color values hex-format, valid color value names (see demo(\"colors\")) name pre-defined color palette. Following options valid colors argument: specified, default color brewer palette used, suitable plot style. \"gs\", greyscale used. \"bw\", plot-type line-plot, plot black/white uses different line types distinguish groups (see package-vignette). colors valid color brewer palette name, related palette used. Use RColorBrewer::display.brewer.() view available palette names. pre-defined color palettes package, see sjPlot-themes details. Else specify color values names vector (e.g. colors = \"#00ff00\" colors = c(\"firebrick\", \"blue\")). geom.size size resp. width geoms (bar width, line thickness point size, depending plot type function). Note bar bin widths mostly need smaller values dot sizes. shape.fill.color Optional color vector, fill-color non-filled shapes shapes Numeric vector shape styles, used map different categories x. title Character vector, used plot title. default, response_labels called retrieve label dependent variable, used title. Use title = \"\" remove title. axis.labels character vector labels used axis labels. Optional argument, since cases, axis labels set automatically. axis.titles character vector length one two, defining title(s) x-axis y-axis. legend.title Character vector, used legend title plots legend. legend.labels character vector labels guide/legend. wrap.title Numeric, determines many chars plot title displayed one line line break inserted. wrap.labels numeric, determines many chars value, variable axis labels displayed one line line break inserted. wrap.legend.title numeric, determines many chars legend's title displayed one line line break inserted. wrap.legend.labels numeric, determines many chars legend labels displayed one line line break inserted. axis.lim Numeric vector length 2, defining range plot axis. Depending plot type, may effect either x- y-axis, . multiple plot outputs (e.g., type = \"eff\" type = \"slope\" plot_model), axis.lim may also list vectors length 2, defining axis limits plot (non-faceted). grid.breaks numeric; sets distance breaks axis, .e. every grid.breaks'th position major grid printed. show.total Logical, TRUE, total summary line aggregated grp added. annotate.total Logical, TRUE show.total = TRUE, total-row figure highlighted slightly shaded background. show.p Logical, adds significance levels values, value variable labels. show.n logical, TRUE, adds total number cases group category labels.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_gpt.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot grouped proportional tables — plot_gpt","text":"ggplot-object.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_gpt.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plot grouped proportional tables — plot_gpt","text":"p-values based chisq.test x            y grp.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_gpt.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot grouped proportional tables — plot_gpt","text":"","code":"if (requireNamespace(\"haven\")) {   data(efc)    # the proportion of dependency levels in female   # elderly, for each family carer's relationship   # to elderly   plot_gpt(efc, e42dep, e16sex, e15relat)    # proportion of educational levels in highest   # dependency category of elderly, for different   # care levels   plot_gpt(efc, c172code, e42dep, n4pstu) }"},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_grid.html","id":null,"dir":"Reference","previous_headings":"","what":"Arrange list of plots as grid — plot_grid","title":"Arrange list of plots as grid — plot_grid","text":"Plot multiple ggplot-objects grid-arranged single plot.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_grid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Arrange list of plots as grid — plot_grid","text":"","code":"plot_grid(x, margin = c(1, 1, 1, 1), tags = NULL)"},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_grid.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Arrange list of plots as grid — plot_grid","text":"x list ggplot-objects. See 'Details'. margin numeric vector length 4, indicating top, right, bottom left margin plot, centimetres. tags Add tags subfigures. Can TRUE (letter tags) character vector containing tags labels.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_grid.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Arrange list of plots as grid — plot_grid","text":"object class gtable.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_grid.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Arrange list of plots as grid — plot_grid","text":"function takes list ggplot-objects argument.          Plotting functions package produce multiple plot          objects (e.g., argument facet.grid) usually          return multiple plots list (return value named plot.list).          arrange plots grid single plot, use plot_grid.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_grid.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Arrange list of plots as grid — plot_grid","text":"","code":"if (require(\"dplyr\") && require(\"gridExtra\")) {   library(ggeffects)   data(efc)    # fit model   fit <- glm(     tot_sc_e ~ c12hour + e17age + e42dep + neg_c_7,     data = efc,     family = poisson   )    # plot marginal effects for each predictor, each as single plot   p1 <- ggpredict(fit, \"c12hour\") %>%     plot(show_y_title = FALSE, show_title = FALSE)   p2 <- ggpredict(fit, \"e17age\") %>%     plot(show_y_title = FALSE, show_title = FALSE)   p3 <- ggpredict(fit, \"e42dep\") %>%     plot(show_y_title = FALSE, show_title = FALSE)   p4 <- ggpredict(fit, \"neg_c_7\") %>%     plot(show_y_title = FALSE, show_title = FALSE)    # plot grid   plot_grid(list(p1, p2, p3, p4))    # plot grid   plot_grid(list(p1, p2, p3, p4), tags = TRUE) } #>  #> Attaching package: ‘ggeffects’ #> The following object is masked _by_ ‘.GlobalEnv’: #>  #>     efc"},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_grpfrq.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot grouped or stacked frequencies — plot_grpfrq","title":"Plot grouped or stacked frequencies — plot_grpfrq","text":"Plot grouped stacked frequencies variables bar/dot,                box violin plots, line plot.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_grpfrq.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot grouped or stacked frequencies — plot_grpfrq","text":"","code":"plot_grpfrq(   var.cnt,   var.grp,   type = c(\"bar\", \"dot\", \"line\", \"boxplot\", \"violin\"),   bar.pos = c(\"dodge\", \"stack\"),   weight.by = NULL,   intr.var = NULL,   title = \"\",   title.wtd.suffix = NULL,   legend.title = NULL,   axis.titles = NULL,   axis.labels = NULL,   legend.labels = NULL,   intr.var.labels = NULL,   wrap.title = 50,   wrap.labels = 15,   wrap.legend.title = 20,   wrap.legend.labels = 20,   geom.size = NULL,   geom.spacing = 0.15,   geom.colors = \"Paired\",   show.values = TRUE,   show.n = TRUE,   show.prc = TRUE,   show.axis.values = TRUE,   show.ci = FALSE,   show.grpcnt = FALSE,   show.legend = TRUE,   show.na = FALSE,   show.summary = FALSE,   drop.empty = TRUE,   auto.group = NULL,   ylim = NULL,   grid.breaks = NULL,   expand.grid = FALSE,   inner.box.width = 0.15,   inner.box.dotsize = 3,   smooth.lines = FALSE,   emph.dots = TRUE,   summary.pos = \"r\",   facet.grid = FALSE,   coord.flip = FALSE,   y.offset = NULL,   vjust = \"bottom\",   hjust = \"center\" )"},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_grpfrq.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot grouped or stacked frequencies — plot_grpfrq","text":"var.cnt Vector counts, frequencies means plotted printed. var.grp Factor cross-classifying variable, var.cnt grouped categories represented var.grp. type Specifies plot type. May abbreviated. \"bar\" simple bars (default) \"dot\" dot plot \"histogram\" histogram (apply grouped frequencies) \"line\" line-styled histogram filled area \"density\" density plot (apply grouped frequencies) \"boxplot\" box plot \"violin\" violin plots bar.pos Indicates whether bars positioned side--side (default), stacked (bar.pos = \"stack\"). May abbreviated. weight.Vector weights applied weight cases. Must vector length input vector. Default NULL, weights used. intr.var interaction variable can used box plots. Divides category indicated var.grp factors intr.var, category var.grp subgrouped intr.var's categories. applies type = \"boxplot\" type = \"violin\". title character vector, used plot title. Depending plot type function, set automatically. title = \"\", title printed. effect-plots, may also character vector length > 1, define titles sub-plot facet. title.wtd.suffix Suffix (string) title, weight.specified, e.g. title.wtd.suffix=\" (weighted)\". Default NULL, title suffix cases weighted. legend.title character vector, used title plot legend. axis.titles character vector length one two, defining title(s) x-axis y-axis. axis.labels character vector labels used axis labels. Optional argument, since cases, axis labels set automatically. legend.labels character vector labels guide/legend. intr.var.labels character vector labels x-axis breaks interaction variables included. labels replace axis.labels. applies, using box violin plots (.e. type = \"boxplot\" \"violin\") intr.var NULL. wrap.title numeric, determines many chars plot title displayed one line line break inserted. wrap.labels numeric, determines many chars value, variable axis labels displayed one line line break inserted. wrap.legend.title numeric, determines many chars legend's title displayed one line line break inserted. wrap.legend.labels numeric, determines many chars legend labels displayed one line line break inserted. geom.size size resp. width geoms (bar width, line thickness point size, depending plot type function). Note bar bin widths mostly need smaller values dot sizes. geom.spacing spacing geoms (.e. bar spacing) geom.colors user defined color geoms. See 'Details' plot_grpfrq. show.values Logical, whether values plotted . show.n logical, TRUE, adds total number cases group category labels. show.prc logical, TRUE (default), percentage values plotted bar FALSE, percentage values removed. show.axis.values logical, whether category, count percentage values axis printed . show.ci Logical, TRUE), adds notches box plot, used compare groups; notches two boxes overlap, medians considered significantly different. show.grpcnt logical, TRUE, count within group added category labels (e.g. \"Cat 1 (n=87)\"). Default value FALSE. show.legend logical, TRUE, depending plot type function, legend added plot. show.na logical, TRUE, NA's (missing values) added output. show.summary logical, TRUE (default), summary chi-squared statistics (see chisq.test), Cramer's V Phi-value etc. shown. cell contains expected values lower five (lower 10 df 1), Fisher's exact test (see fisher.test) computed instead chi-squared test. table's matrix larger 2x2, Fisher's exact test Monte Carlo simulation computed. drop.empty Logical, TRUE variable's values labeled, values / factor levels occurrence data omitted output. FALSE, labeled values observations still printed table (frequency 0). auto.group numeric value, indicating minimum amount unique values count variable, automatic grouping smaller units done (see group_var). Default value auto.group NULL, .e. auto-grouping . See group_var examples grouping. ylim numeric vector length two, defining lower upper axis limits y scale. default, argument set NULL, .e. y-axis fits required range data. grid.breaks numeric; sets distance breaks axis, .e. every grid.breaks'th position major grid printed. expand.grid logical, TRUE, plot grid expanded, .e. small margin axes plotting region. Default FALSE. inner.box.width width inner box plot plotted inside violin plots. applies type = \"violin\". Default value 0.15 inner.box.dotsize size mean dot insie violin box plot. Applies type = \"violin\" \"boxplot\". smooth.lines prints smooth line curve. applies, argument type = \"line\". emph.dots logical, TRUE, groups dots dot-plot highlighted shaded rectangle. summary.pos position model summary printed show.summary TRUE. Default \"r\", .e. printed upper right corner. Use \"l\" upper left corner. facet.grid TRUE arrange lay multiple plots grid integrated single plot. argument calls facet_wrap facet_grid arrange plots. Use plot_grid plot multiple plot-objects arranged grid grid.arrange. coord.flip logical, TRUE, x y axis swapped. y.offset numeric, offset text labels alignment adjusted top/bottom geom (see hjust vjust). vjust character vector, indicating vertical position value labels. Allowed values vjust aesthetics ggplot2: \"left\", \"center\", \"right\", \"bottom\", \"middle\", \"top\" new options like \"inward\" \"outward\", align text towards away center plot respectively. hjust character vector, indicating horizontal position value labels. Allowed values vjust aesthetics ggplot2: \"left\", \"center\", \"right\", \"bottom\", \"middle\", \"top\" new options like \"inward\" \"outward\", align text towards away center plot respectively.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_grpfrq.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot grouped or stacked frequencies — plot_grpfrq","text":"ggplot-object.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_grpfrq.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plot grouped or stacked frequencies — plot_grpfrq","text":"geom.colors may character vector color values          hex-format, valid color value names (see demo(\"colors\")          name color brewer palette.          Following options valid geom.colors argument: specified, default color brewer palette used, suitable plot style (.e. diverging likert scales, qualitative grouped bars etc.). \"gs\", greyscale used. \"bw\", plot-type line-plot, plot black/white uses different line types distinguish groups (see package-vignette). geom.colors valid color brewer palette name, related palette used. Use RColorBrewer::display.brewer.() view available palette names. Else specify color values names vector (e.g. geom.colors = c(\"#f00000\", \"#00ff00\")).","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_grpfrq.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot grouped or stacked frequencies — plot_grpfrq","text":"","code":"data(efc) plot_grpfrq(efc$e17age, efc$e16sex, show.values = FALSE)   # boxplot plot_grpfrq(efc$e17age, efc$e42dep, type = \"box\") #> Warning: Solution may be nonunique #> Warning: Solution may be nonunique #> Warning: Solution may be nonunique #> Warning: Solution may be nonunique #> Warning: Solution may be nonunique #> Warning: Solution may be nonunique   # grouped bars plot_grpfrq(efc$e42dep, efc$e16sex, title = NULL)   # box plots with interaction variable plot_grpfrq(efc$e17age, efc$e42dep, intr.var = efc$e16sex, type = \"box\") #> Warning: Solution may be nonunique #> Warning: Solution may be nonunique #> Warning: Solution may be nonunique   # Grouped bar plot plot_grpfrq(efc$neg_c_7, efc$e42dep, show.values = FALSE)   # same data as line plot plot_grpfrq(efc$neg_c_7, efc$e42dep, type = \"line\")   # show ony categories where we have data (i.e. drop zero-counts) library(dplyr) efc <- dplyr::filter(efc, e42dep %in% c(3,4)) plot_grpfrq(efc$c161sex, efc$e42dep, drop.empty = TRUE)   # show all categories, even if not in data plot_grpfrq(efc$c161sex, efc$e42dep, drop.empty = FALSE)"},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_kfold_cv.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot model fit from k-fold cross-validation — plot_kfold_cv","title":"Plot model fit from k-fold cross-validation — plot_kfold_cv","text":"function plots aggregated residuals k-fold cross-validated   models outcome. allows evaluate model performs   according - underestimation outcome.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_kfold_cv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot model fit from k-fold cross-validation — plot_kfold_cv","text":"","code":"plot_kfold_cv(data, formula, k = 5, fit)"},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_kfold_cv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot model fit from k-fold cross-validation — plot_kfold_cv","text":"data data frame, used split data k training-test-pairs. formula model formula, used fit linear models (lm) k training data sets. Use fit specify fitted model (also models linear models), used compute cross validation. fit missing, formula ignored. k Number folds. fit Model object, used compute cross validation. fit missing, formula ignored. Currently, linear, poisson negative binomial regression models supported.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_kfold_cv.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plot model fit from k-fold cross-validation — plot_kfold_cv","text":"function, first, generates k cross-validated test-training            pairs            fits model, specified formula- fit-            argument, training data sets.             , test data used predict outcome            models fit training data, residuals            test data plotted observed values (outcome)            test data (note: poisson negative binomial models,            deviance residuals calculated). plot can used validate model            see, whether - (residuals > 0) underestimates            (residuals < 0) model's outcome.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_kfold_cv.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Plot model fit from k-fold cross-validation — plot_kfold_cv","text":"Currently, linear, poisson negative binomial regression models supported.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_kfold_cv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot model fit from k-fold cross-validation — plot_kfold_cv","text":"","code":"data(efc)  plot_kfold_cv(efc, neg_c_7 ~ e42dep + c172code + c12hour) #> `geom_smooth()` using formula = 'y ~ x' #> Warning: Removed 117 rows containing non-finite outside the scale range #> (`stat_smooth()`). #> Warning: Removed 117 rows containing missing values or values outside the scale range #> (`geom_point()`).  plot_kfold_cv(mtcars, mpg ~.) #> `geom_smooth()` using formula = 'y ~ x'   # for poisson models. need to fit a model and use 'fit'-argument fit <- glm(tot_sc_e ~ neg_c_7 + c172code, data = efc, family = poisson) plot_kfold_cv(efc, fit = fit) #> `geom_smooth()` using formula = 'y ~ x' #> Warning: pseudoinverse used at -0.045 #> Warning: neighborhood radius 1.045 #> Warning: reciprocal condition number  0 #> Warning: There are other near singularities as well. 1 #> Warning: pseudoinverse used at -0.045 #> Warning: neighborhood radius 1.045 #> Warning: reciprocal condition number  0 #> Warning: There are other near singularities as well. 1   # and for negative binomial models fit <- MASS::glm.nb(tot_sc_e ~ neg_c_7 + c172code, data = efc) plot_kfold_cv(efc, fit = fit) #> `geom_smooth()` using formula = 'y ~ x' #> Warning: pseudoinverse used at -0.045 #> Warning: neighborhood radius 1.045 #> Warning: reciprocal condition number  1.0154e-30 #> Warning: There are other near singularities as well. 1 #> Warning: pseudoinverse used at -0.045 #> Warning: neighborhood radius 1.045 #> Warning: reciprocal condition number  1.0154e-30 #> Warning: There are other near singularities as well. 1"},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_likert.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot likert scales as centered stacked bars — plot_likert","title":"Plot likert scales as centered stacked bars — plot_likert","text":"Plot likert scales centered stacked bars.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_likert.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot likert scales as centered stacked bars — plot_likert","text":"","code":"plot_likert(   items,   groups = NULL,   groups.titles = \"auto\",   title = NULL,   legend.title = NULL,   legend.labels = NULL,   axis.titles = NULL,   axis.labels = NULL,   catcount = NULL,   cat.neutral = NULL,   sort.frq = NULL,   weight.by = NULL,   title.wtd.suffix = NULL,   wrap.title = 50,   wrap.labels = 30,   wrap.legend.title = 30,   wrap.legend.labels = 28,   geom.size = 0.6,   geom.colors = \"BrBG\",   cat.neutral.color = \"grey70\",   intercept.line.color = \"grey50\",   reverse.colors = FALSE,   values = \"show\",   show.n = TRUE,   show.legend = TRUE,   show.prc.sign = FALSE,   grid.range = 1,   grid.breaks = 0.2,   expand.grid = TRUE,   digits = 1,   reverse.scale = FALSE,   coord.flip = TRUE,   sort.groups = TRUE,   legend.pos = \"bottom\",   rel_heights = 1,   group.legend.options = list(nrow = NULL, byrow = TRUE),   cowplot.options = list(label_x = 0.01, hjust = 0, align = \"v\") )"},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_likert.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot likert scales as centered stacked bars — plot_likert","text":"items Data frame, grouped data frame, column representing one item. groups (optional) Must vector length ncol(items), item vector represents group number related columns items. See 'Examples'. groups.titles (optional, used groups supplied) Titles factor group used table caption component-table. Must character vector length length(unique(groups)). Default \"auto\", means table standard caption Component x. Use NULL use names supplied groups use FALSE suppress table captions. title character vector, used plot title. Depending plot type function, set automatically. title = \"\", title printed. effect-plots, may also character vector length > 1, define titles sub-plot facet. legend.title character vector, used title plot legend. legend.labels character vector labels guide/legend. axis.titles character vector length one two, defining title(s) x-axis y-axis. axis.labels character vector labels used axis labels. Optional argument, since cases, axis labels set automatically. catcount optional, amount categories items (e.g. \"strongly disagree\", \"disagree\", \"agree\" \"strongly agree\" catcount = 4). Note argument applies \"valid\" answers, .e. additional neutral category (see cat.neutral) like \"know\", count catcount (e.g. \"strongly disagree\", \"disagree\", \"agree\", \"strongly agree\" neutral category \"know\" still mean catcount = 4). See 'Note'. cat.neutral neutral category (like \"know\" etc.), specify index number (value) category. Else, set cat.neutral = NULL (default). proportions neutral category answers plotted grey bars left side figure. sort.frq Indicates whether items items ordered total sum positive negative answers. \"pos.asc\" order ascending sum positive answers \"pos.desc\" order descending sum positive answers \"neg.asc\" sorting ascending negative answers \"neg.desc\" sorting descending negative answers NULL (default) sorting weight.Vector weights applied weight cases. Must vector length input vector. Default NULL, weights used. title.wtd.suffix Suffix (string) title, weight.specified, e.g. title.wtd.suffix=\" (weighted)\". Default NULL, title suffix cases weighted. wrap.title numeric, determines many chars plot title displayed one line line break inserted. wrap.labels numeric, determines many chars value, variable axis labels displayed one line line break inserted. wrap.legend.title numeric, determines many chars legend's title displayed one line line break inserted. wrap.legend.labels numeric, determines many chars legend labels displayed one line line break inserted. geom.size size resp. width geoms (bar width, line thickness point size, depending plot type function). Note bar bin widths mostly need smaller values dot sizes. geom.colors user defined color geoms. See 'Details' plot_grpfrq. cat.neutral.color Color neutral category, plotted (see cat.neutral). intercept.line.color Color vertical intercept line divides positive negative values. reverse.colors logical, TRUE, color scale geom.colors reversed, positive negative values switch colors. values Determines style position percentage value labels bars: \"show\" (default) shows percentage value labels middle category bar \"hide\" hides value labels, percentage values bars printed \"sum.inside\" shows sums percentage values negative positive values prints inside end bar \"sum.outside\" shows sums percentage values negative positive values prints outside end bar show.n logical, TRUE, adds total number cases group category labels. show.legend logical, TRUE, depending plot type function, legend added plot. show.prc.sign logical, TRUE, %-signs value labels shown. grid.range Numeric, limits x-axis-range, proportion 100. Default 1, x-scale ranges zero 100% sides center. Can alternatively supplied vector 2 positive numbers (e.g. grid.range = c(1, .8)) set left right limit separately. can use values beyond 1 (100%) case bar labels printed exceed axis range. E.g. grid.range = 1.4 set axis -140 +140%, however, (valid) axis labels -100 +100% printed. Neutral categories adjusted left limit. grid.breaks numeric; sets distance breaks axis, .e. every grid.breaks'th position major grid printed. expand.grid logical, TRUE, plot grid expanded, .e. small margin axes plotting region. Default FALSE. digits Numeric, amount digits decimal point rounding estimates values. reverse.scale logical, TRUE, ordering categories reversed, positive negative values switch position. coord.flip logical, TRUE, x y axis swapped. sort.groups (optional, used groups supplied) logical, groups sorted according values supplied groups. Defaults TRUE. legend.pos (optional, used groups supplied) Defines legend position. Possible values c(\"bottom\", \"top\", \"\", \"\", \"none\"). one group option set \"\" legends printed defined set_theme. rel_heights (optional, used groups supplied) option can used adjust height subplots. bars subplots can different heights due differing number items due legend placement. can adjusted . Takes vector numbers, one plot. Values evaluated relative . group.legend.options (optional, used groups supplied) List options passed guide_legend. notable options byrow=T (default), order categories row wise. group.legend.options = list(nrow = 1) categories can forced single row. cowplot.options (optional, used groups supplied) List label options passed plot_grid.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_likert.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot likert scales as centered stacked bars — plot_likert","text":"ggplot-object.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_likert.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Plot likert scales as centered stacked bars — plot_likert","text":"Note even numbers categories possible plot, \"positive\"        \"negative\" values can splitted two halfs. neutral category (like \"know\")        can used, must indicated cat.neutral.         catcount-argument indicates many item categories        Likert scale. Normally, argument can ignored amount        valid categories retrieved automatically. However, sometimes (instance,        certain category missing items), auto-detection amount        categories fails. cases, specify amount categories        catcount-argument.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_likert.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot likert scales as centered stacked bars — plot_likert","text":"","code":"if (requireNamespace(\"ggrepel\") && requireNamespace(\"sjmisc\")) { library(sjmisc) data(efc) # find all variables from COPE-Index, which all have a \"cop\" in their # variable name, and then plot that subset as likert-plot mydf <- find_var(efc, pattern = \"cop\", out = \"df\")  plot_likert(mydf)  plot_likert(   mydf,   grid.range = c(1.2, 1.4),   expand.grid = FALSE,   values = \"sum.outside\",   show.prc.sign = TRUE )  # Plot in groups  plot_likert(mydf, c(2,1,1,1,1,2,2,2,1))  if (require(\"parameters\") && require(\"nFactors\")) {   groups <- parameters::principal_components(mydf)   plot_likert(mydf, groups = parameters::closest_component(groups)) }  plot_likert(mydf,             c(rep(\"B\", 4), rep(\"A\", 5)),             sort.groups = FALSE,             grid.range = c(0.9, 1.1),             geom.colors = \"RdBu\",             rel_heights = c(6, 8),             wrap.labels = 40,             reverse.scale = TRUE)  # control legend items six_cat_example = data.frame(   matrix(sample(1:6, 600, replace = TRUE),   ncol = 6) )  if (FALSE) { # \\dontrun{ six_cat_example <-   six_cat_example %>%   dplyr::mutate_all(~ordered(.,labels = c(\"+++\",\"++\",\"+\",\"-\",\"--\",\"---\")))  # Old default plot_likert(   six_cat_example,   groups = c(1, 1, 1, 2, 2, 2),   group.legend.options = list(nrow = 2, byrow = FALSE) )  # New default plot_likert(six_cat_example, groups = c(1, 1, 1, 2, 2, 2))  # Single row plot_likert(   six_cat_example,   groups = c(1, 1, 1, 2, 2, 2),   group.legend.options = list(nrow = 1) )} # } } #> Loading required namespace: ggrepel #> Loading required package: parameters #> Loading required package: nFactors #> Loading required package: lattice #>  #> Attaching package: ‘nFactors’ #> The following object is masked from ‘package:lattice’: #>  #>     parallel"},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_model.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot regression models — plot_model","title":"Plot regression models — plot_model","text":"plot_model() creates plots regression models, either   estimates (-called forest dot whisker plots) marginal effects.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot regression models — plot_model","text":"","code":"plot_model(   model,   type = c(\"est\", \"re\", \"eff\", \"emm\", \"pred\", \"int\", \"std\", \"std2\", \"slope\", \"resid\",     \"diag\"),   transform,   terms = NULL,   sort.est = NULL,   rm.terms = NULL,   group.terms = NULL,   order.terms = NULL,   pred.type = c(\"fe\", \"re\"),   mdrt.values = c(\"minmax\", \"meansd\", \"zeromax\", \"quart\", \"all\"),   ri.nr = NULL,   title = NULL,   axis.title = NULL,   axis.labels = NULL,   legend.title = NULL,   wrap.title = 50,   wrap.labels = 25,   axis.lim = NULL,   grid.breaks = NULL,   ci.lvl = NULL,   se = NULL,   robust = FALSE,   vcov.fun = NULL,   vcov.type = NULL,   vcov.args = NULL,   colors = \"Set1\",   show.intercept = FALSE,   show.values = FALSE,   show.p = TRUE,   show.data = FALSE,   show.legend = TRUE,   show.zeroinf = TRUE,   value.offset = NULL,   value.size,   jitter = NULL,   digits = 2,   dot.size = NULL,   line.size = NULL,   vline.color = NULL,   p.threshold = c(0.05, 0.01, 0.001),   p.val = NULL,   p.adjust = NULL,   grid,   case,   auto.label = TRUE,   prefix.labels = c(\"none\", \"varname\", \"label\"),   bpe = \"median\",   bpe.style = \"line\",   bpe.color = \"white\",   ci.style = c(\"whisker\", \"bar\"),   std.response = TRUE,   ... )  get_model_data(   model,   type = c(\"est\", \"re\", \"eff\", \"pred\", \"int\", \"std\", \"std2\", \"slope\", \"resid\", \"diag\"),   transform,   terms = NULL,   sort.est = NULL,   rm.terms = NULL,   group.terms = NULL,   order.terms = NULL,   pred.type = c(\"fe\", \"re\"),   ri.nr = NULL,   ci.lvl = NULL,   colors = \"Set1\",   grid,   case = \"parsed\",   digits = 2,   ... )"},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot regression models — plot_model","text":"model regression model object. Depending type, many kinds models supported, e.g. packages like stats, lme4, nlme, rstanarm, survey, glmmTMB, MASS, brms etc. type Type plot. three groups plot-types: Coefficients (related vignette) type = \"est\" Forest-plot estimates. fitted model   contains one predictor, slope-line plotted. type = \"re\" mixed effects models, plots random   effects. type = \"std\" Forest-plot standardized coefficients. type = \"std2\" Forest-plot standardized coefficients,   however, standardization done dividing two SD (see 'Details'). Marginal Effects  (related vignette) type = \"pred\" Predicted values (marginal effects)   specific model terms. See ggpredict details. type = \"eff\" Similar type = \"pred\", however,   discrete predictors held constant proportions (reference   level). See ggeffect details. type = \"emm\" Similar type = \"eff\", see   ggemmeans details. type = \"int\" Marginal effects interaction terms   model. Model diagnostics type = \"slope\" Slope coefficients single   predictor, response (linear relationship model   term response). See 'Details'. type = \"resid\" Slope coefficients single   predictor, residuals (linear relationship model   term residuals). See 'Details'. type = \"diag\" Check model assumptions. See 'Details'. Note: mixed models, diagnostic plots like linear relationship check Homoscedasticity, take uncertainty random effects account, based fixed effects part model. transform character vector, naming function applied estimates confidence intervals. default, transform automatically use \"exp\" transformation applicable classes model (e.g. logistic poisson regression). Estimates linear models remain untransformed. Use NULL want raw, non-transformed estimates. terms Character vector names terms model  plotted. argument depends plot-type: Coefficients Select terms plotted.      term removed output. Note term names must match      names model's coefficients. factors, means      variable name suffixed related factor level,      category counts one term. E.g. rm.terms = \"t_name [2,3]\"      remove terms \"t_name2\" \"t_name3\" (assuming      variable t_name categorical least      factor levels 2 3). Another example      iris-dataset: terms = \"Species\" work, instead      write terms = \"Species [versicolor,virginica]\"      remove two levels, terms = \"Speciesversicolor\"      just want remove level versicolor plot. Marginal Effects terms indicates      terms marginal effects displayed. least one term      required calculate effects, maximum length three terms,      second third term indicate groups, .e. predictions      first term grouped levels second (third) term.      terms may also indicate higher order terms (e.g. interaction      terms). Indicating levels square brackets allows selecting      specific groups. Term name levels brackets must separated      whitespace character, e.g. terms = c(\"age\", \"education [1,3]\").      also possible specify range numeric values      predictions colon, instance terms = c(\"education [1,3]\",      \"age [30:50]\"). Furthermore, possible specify function name.      Values predictions transformed, e.g.      terms = \"income [exp]\". useful model predictors      transformed fitting model back-transformed      original scale predictions. Finally, numeric vectors      specific values given, \"pretty range\" calculated, avoid      memory allocation problems vectors many unique values.      numeric vector specified second third term (.e. vector      represents grouping structure), representative values (see      values_at) chosen. values      numeric vector used compute predictions, may use      e.g. terms = \"age []\". details, see      ggpredict. sort.est Determines way estimates sorted plot: NULL (default), sorting done estimates sorted order appear model formula. TRUE, estimates sorted descending order, highest estimate top. sort.est = \"sort.\", estimates re-sorted coefficient (applies type = \"re\" grid = FALSE), .e. estimates random effects predictor sorted plotted plot. type = \"re\", specify predictor's / coefficient's name sort estimates according random effect. rm.terms Character vector names indicate terms removed plot. Counterpart terms. rm.terms = \"t_name\" remove term t_name. Default NULL, .e. terms used. factors, levels removed plot need explicitely indicated square brackets, match model's coefficient names, e.g. rm.terms = \"t_name [2,3]\" remove terms \"t_name2\" \"t_name3\" (assuming variable t_name categorical least factor levels 2 3). Another example iris dataset rm.terms = \"Species [versicolor,virginica]\". Note rm.terms-argument apply Marginal Effects plots. group.terms Numeric vector group indices, group coefficients. group coefficients gets color (see 'Examples'). order.terms Numeric vector, indicating order coefficients plotted. See examples package-vignette. pred.type Character, applies Marginal Effects plots mixed effects models. Indicates whether predicted values conditioned random effects (pred.type = \"re\") fixed effects (pred.type = \"fe\", default). details, see documentation type-argument ggpredict. mdrt.values Indicates values moderator variable used plotting interaction terms (.e. type = \"int\"). \"minmax\" (default) minimum maximum values (lower upper bounds) moderator used plot interaction independent variable moderator(s). \"meansd\" uses mean value moderator well one standard deviation mean value plot effect moderator independent variable (following convention suggested Cohen Cohen popularized Aiken West (1991), .e. using mean, value one standard deviation , value one standard deviation mean values moderator, see Grace-Martin K: 3 Tips Make Interpreting Moderation Effects Easier). \"zeromax\" similar \"minmax\" option, however, 0 always used minimum value moderator. may useful predictors empirical zero-value, absence moderation simulated using 0 minimum. \"quart\" calculates uses quartiles (lower, median upper) moderator value. \"\" uses values moderator variable. ri.nr Numeric vector. type = \"re\" fitted model one random intercept, ri.nr indicates random effects random intercept (: list elements ranef) plotted. Default NULL, random effects plotted. title Character vector, used plot title. default, response_labels called retrieve label dependent variable, used title. Use title = \"\" remove title. axis.title Character vector length one two (depending plot function type), used title(s) x y axis. specified, default labelling  chosen. Note: plot types may support argument sufficiently. cases, use returned ggplot-object add axis titles manually labs. Use axis.title = \"\" remove axis titles. axis.labels Character vector labels model terms, used axis labels. default, term_labels called retrieve labels coefficients, used axis labels. Use axis.labels = \"\" auto.label = FALSE use variable names labels instead. axis.labels named vector, axis labels (default, names model's coefficients) matched names axis.label. ensures labels always match related axis value, matter way axis labels sorted. legend.title Character vector, used legend title plots legend. wrap.title Numeric, determines many chars plot title displayed one line line break inserted. wrap.labels Numeric, determines many chars value, variable axis labels displayed one line line break inserted. axis.lim Numeric vector length 2, defining range plot axis. Depending plot-type, may effect either x- y-axis. Marginal Effects plots, axis.lim may also list two vectors length 2, defining axis limits x y axis. grid.breaks Numeric value vector; grid.breaks single value, sets distance breaks axis every grid.breaks'th position, major grid line plotted. grid.breaks vector, values used define axis positions major grid lines. ci.lvl Numeric, level confidence intervals (error bars). Use ci.lvl = NA remove error bars. stanreg-models, ci.lvl defines (outer) probability credible interval plotted (see ci). default, stanreg-models printed two intervals: \"inner\" interval, defaults 50%-CI; \"outer\" interval, defaults 89%-CI. ci.lvl affects outer interval cases. See prob.inner prob.outer ...-argument details. se Logical, TRUE, standard errors also printed. robust standard errors required, use arguments vcov.fun, vcov.type vcov.args (see standard_error details), use argument robust shortcut. se overrides ci.lvl: NULL, arguments ci.lvl transform ignored. Currently, se applies Coefficients plots. robust Deprecated. Please use vcov.fun directly specify estimation variance-covariance matrix. vcov.fun Variance-covariance matrix used compute uncertainty estimates (e.g., robust standard errors). argument accepts covariance matrix, function returns covariance matrix, string identifies function used compute covariance matrix. See model_parameters(). vcov.type Deprecated. type-argument now included vcov.args. vcov.args List arguments passed function identified vcov.fun argument. function typically supplied sandwich clubSandwich packages. Please refer documentation (e.g., ?sandwich::vcovHAC) see list available arguments. colors May character vector color values hex-format, valid color value names (see demo(\"colors\")) name pre-defined color palette. Following options valid colors argument: specified, default color brewer palette used, suitable plot style. \"gs\", greyscale used. \"bw\", plot-type line-plot, plot black/white uses different line types distinguish groups (see package-vignette). colors valid color brewer palette name, related palette used. Use RColorBrewer::display.brewer.() view available palette names. pre-defined color palettes package, see sjPlot-themes details. Else specify color values names vector (e.g. colors = \"#00ff00\" colors = c(\"firebrick\", \"blue\")). show.intercept Logical, TRUE, intercept fitted model also plotted. Default FALSE. transform = \"exp\", please note due exponential transformation estimates, intercept cases non-finite plot can created. show.values Logical, whether values plotted . show.p Logical, adds asterisks indicate significance level estimates value labels. show.data Logical, Marginal Effects plots, also plots raw data points. show.legend Marginal Effects plots, shows hides legend. show.zeroinf Logical, TRUE, shows zero-inflation part hurdle- zero-inflated models. value.offset Numeric, offset text labels adjust position relative dots lines. value.size Numeric, indicates size value labels. Can used plot types argument show.values applicable, e.g. value.size = 4. jitter Numeric, 0 1. show.data = TRUE, can add small amount random variation location data point. jitter indicates width, .e. much bin's width occupied jittered values. digits Numeric, amount digits decimal point rounding estimates values. dot.size Numeric, size dots indicate point estimates. line.size Numeric, size lines indicate error bars. vline.color Color vertical \"zero effect\" line. Default color inherited current theme. p.threshold Numeric vector length 3, indicating treshold annotating p-values asterisks. applies p.style = \"asterisk\". p.val Character specifying method used calculate p-values. Defaults \"profile\" glm/polr models, otherwise \"wald\". p.adjust Character vector, NULL, indicates method adjust p-values. See p.adjust details. grid Logical, TRUE, multiple plots plotted grid layout. case Desired target case. Labels automatically converted specified character case. See snakecase::to_any_case() details argument. default, case specified, set \"parsed\", unless prefix.labels \"none\". prefix.labels either \"label\" (\"l\") \"varname\" (\"v\") case specified, set NULL - convenient default prefixing labels. auto.label Logical, TRUE (default),  data labelled,  term_labels called retrieve labels  coefficients, used predictor labels. data  labelled, format_parameters()  used create pretty labels. auto.label = FALSE, original variable names value labels (factor levels) used. prefix.labels Indicates whether value labels categorical variables prefixed, e.g. variable name variable label. See argument prefix term_labels details. bpe Stan-models (fitted rstanarm- brms-package), Bayesian point estimate , default, median posterior distribution. Use bpe define functions calculate Bayesian point estimate. bpe needs character naming specific function, passed fun-argument typical_value. , bpe = \"mean\" calculate mean value posterior distribution. bpe.style Stan-models (fitted rstanarm- brms-package), Bayesian point estimate indicated small, vertical line default. Use bpe.style = \"dot\" plot dot instead line point estimate. bpe.color Character vector, indicating color Bayesian point estimate. Setting bpe.color = NULL inherit color mapped aesthetic match geom's color. ci.style Character vector, defining whether inner outer intervals Bayesion models shown boxplot-style (\"whisker\") bars different alpha-levels (\"bar\"). std.response Logical, whether response variable also standardized standardized coefficients requested. Setting std.response = TRUE show.std = TRUE behave complete data standardized fitting model. ... arguments, passed various functions. list supported arguments description detail. prob.inner prob.outer Stan-models     (fitted rstanarm- brms-package) coefficients     plot-types, can specify numeric values 0 1     prob.inner prob.outer, used inner     outer probabilities uncertainty intervals (HDI). default,     inner probability 0.5 outer probability 0.89 (unless     ci.lvl specified - case, ci.lvl used outer     probability). size.inner Stan-models Coefficients     plot-types, can specify width bar inner     probabilities. Default 0.1. Setting size.inner = 0     removes inner probability regions. width, alpha, scale Passed     geom_errorbar() geom_density_ridges(),     forest diagnostic plots. width, alpha, dot.alpha, dodge log.y Passed     plot.ggeffects Marginal Effects     plots. show.loess Logical, diagnostic plot-types \"slope\"     \"resid\", adds (hides) loess-smoothed line plot. Marginal Effects plot-types plotting marginal effects,     arguments also passed ggpredict,     ggeffect plot.ggeffects. Case conversion labels case conversion labels (see argument     case), arguments sep_in sep_out passed     snakecase::to_any_case().     applies automatically retrieved term labels,     term labels provided axis.labels-argument.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot regression models — plot_model","text":"Depending plot-type, plot_model() returns   ggplot-object list objects. get_model_data   returns associated data plot-object tidy data frame,   (depending plot-type) list data frames.","code":""},{"path":[]},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_model.html","id":"different-plot-types","dir":"Reference","previous_headings":"","what":"Different Plot Types","title":"Plot regression models — plot_model","text":"type = \"std\" Plots standardized estimates. See details . type = \"std2\" Plots standardized estimates, however,     standardization follows Gelman's (2008) suggestion, rescaling     estimates dividing two standard deviations instead just one.     Resulting coefficients directly comparable untransformed     binary predictors. type = \"pred\" Plots estimated marginal means (marginal effects).     Simply wraps ggpredict. See also     package-vignette. type = \"eff\" Plots estimated marginal means (marginal effects).     Simply wraps ggeffect. See also     package-vignette. type = \"int\" shortcut marginal effects plots,     interaction terms automatically detected used     terms-argument. Furthermore, moderator variable (second     - third - term interaction) continuous, type = \"int\"     automatically chooses useful values based mdrt.values-argument,     passed terms. , ggpredict     called. type = \"int\" plots interaction term appears     first formula along x-axis, second (possibly     third) variable interaction used grouping factor(s)     (moderating variable). Use type = \"pred\" type = \"eff\"     specify certain order terms-argument indicate     variable(s) used moderator. See also     package-vignette. type = \"slope\" type = \"resid\" Simple diagnostic-plots,   linear model single predictor plotted   response variable, model's residuals. Additionally, loess-smoothed   line added plot. main purpose plots check whether   relationship outcome (residuals) predictor roughly   linear . Since plots based simple linear regression   one model predictor moment, slopes (.e. coefficients) may   differ coefficients complete model. type = \"diag\" Stan-models, plots prior versus   posterior samples. linear (mixed) models, plots   multicollinearity-check (Variance Inflation Factors), QQ-plots,   checks normal distribution residuals homoscedasticity   (constant variance residuals) shown. generalized   linear mixed models, returns QQ-plot random effects.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_model.html","id":"standardized-estimates","dir":"Reference","previous_headings":"","what":"Standardized Estimates","title":"Plot regression models — plot_model","text":"Default standardization done completely refitting model   standardized data. Hence, approach equal standardizing   variables fitting model, particularly recommended   complex models include interactions transformations (e.g., polynomial   spline terms). type = \"std2\", standardization estimates   follows Gelman's (2008)   suggestion, rescaling estimates dividing two standard deviations   instead just one. Resulting coefficients directly comparable   untransformed binary predictors.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_model.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Plot regression models — plot_model","text":"Gelman (2008) \"Scaling regression inputs dividing two   standard deviations.\" Statistics Medicine 27: 2865-2873.   http://www.stat.columbia.edu/~gelman/research/published/standardizing7.pdf      Aiken West (1991). Multiple Regression: Testing Interpreting Interactions.","code":""},{"path":[]},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_models.html","id":null,"dir":"Reference","previous_headings":"","what":"Forest plot of multiple regression models — plot_models","title":"Forest plot of multiple regression models — plot_models","text":"Plot compare regression coefficients confidence   intervals multiple regression models one plot.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_models.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Forest plot of multiple regression models — plot_models","text":"","code":"plot_models(   ...,   transform = NULL,   std.est = NULL,   std.response = TRUE,   rm.terms = NULL,   title = NULL,   m.labels = NULL,   legend.title = \"Dependent Variables\",   legend.pval.title = \"p-level\",   axis.labels = NULL,   axis.title = NULL,   axis.lim = NULL,   wrap.title = 50,   wrap.labels = 25,   wrap.legend.title = 20,   grid.breaks = NULL,   dot.size = 3,   line.size = NULL,   value.size = NULL,   spacing = 0.4,   colors = \"Set1\",   show.values = FALSE,   show.legend = TRUE,   show.intercept = FALSE,   show.p = TRUE,   p.shape = FALSE,   p.threshold = c(0.05, 0.01, 0.001),   p.adjust = NULL,   ci.lvl = 0.95,   robust = FALSE,   vcov.fun = NULL,   vcov.type = c(\"HC3\", \"const\", \"HC\", \"HC0\", \"HC1\", \"HC2\", \"HC4\", \"HC4m\", \"HC5\"),   vcov.args = NULL,   vline.color = NULL,   digits = 2,   grid = FALSE,   auto.label = TRUE,   prefix.labels = c(\"none\", \"varname\", \"label\") )"},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_models.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Forest plot of multiple regression models — plot_models","text":"... One regression models, including glm's mixed models. May also list fitted models. See 'Examples'. transform character vector, naming function applied estimates confidence intervals. default, transform automatically use \"exp\" transformation applicable classes model (e.g. logistic poisson regression). Estimates linear models remain untransformed. Use NULL want raw, non-transformed estimates. std.est Choose whether standardized coefficients used plotting. Default standardization (std.est = NULL). May \"std\" standardized beta values \"std2\", standardization done rescaling estimates dividing two sd. std.response Logical, whether response variable also standardized standardized coefficients requested. Setting std.response = TRUE show.std = TRUE behave complete data standardized fitting model. rm.terms Character vector names indicate terms removed plot. Counterpart terms. rm.terms = \"t_name\" remove term t_name. Default NULL, .e. terms used. factors, levels removed plot need explicitely indicated square brackets, match model's coefficient names, e.g. rm.terms = \"t_name [2,3]\" remove terms \"t_name2\" \"t_name3\" (assuming variable t_name categorical least factor levels 2 3). Another example iris dataset rm.terms = \"Species [versicolor,virginica]\". Note rm.terms-argument apply Marginal Effects plots. title Character vector, used plot title. default, response_labels called retrieve label dependent variable, used title. Use title = \"\" remove title. m.labels Character vector, used indicate different models plot's legend. specified, labels dependent variables model used. legend.title Character vector, used legend title plots legend. legend.pval.title Character vector, used title plot legend indicates p-values. Default \"p-level\". applies p.shape = TRUE. axis.labels Character vector labels model terms, used axis labels. default, term_labels called retrieve labels coefficients, used axis labels. Use axis.labels = \"\" auto.label = FALSE use variable names labels instead. axis.labels named vector, axis labels (default, names model's coefficients) matched names axis.label. ensures labels always match related axis value, matter way axis labels sorted. axis.title Character vector length one two (depending plot function type), used title(s) x y axis. specified, default labelling  chosen. Note: plot types may support argument sufficiently. cases, use returned ggplot-object add axis titles manually labs. Use axis.title = \"\" remove axis titles. axis.lim Numeric vector length 2, defining range plot axis. Depending plot-type, may effect either x- y-axis. Marginal Effects plots, axis.lim may also list two vectors length 2, defining axis limits x y axis. wrap.title Numeric, determines many chars plot title displayed one line line break inserted. wrap.labels Numeric, determines many chars value, variable axis labels displayed one line line break inserted. wrap.legend.title numeric, determines many chars legend's title displayed one line line break inserted. grid.breaks Numeric value vector; grid.breaks single value, sets distance breaks axis every grid.breaks'th position, major grid line plotted. grid.breaks vector, values used define axis positions major grid lines. dot.size Numeric, size dots indicate point estimates. line.size Numeric, size lines indicate error bars. value.size Numeric, indicates size value labels. Can used plot types argument show.values applicable, e.g. value.size = 4. spacing Numeric, spacing dots error bars plotted fitted models. Default 0.3. colors May character vector color values hex-format, valid color value names (see demo(\"colors\")) name pre-defined color palette. Following options valid colors argument: specified, default color brewer palette used, suitable plot style. \"gs\", greyscale used. \"bw\", plot-type line-plot, plot black/white uses different line types distinguish groups (see package-vignette). colors valid color brewer palette name, related palette used. Use RColorBrewer::display.brewer.() view available palette names. pre-defined color palettes package, see sjPlot-themes details. Else specify color values names vector (e.g. colors = \"#00ff00\" colors = c(\"firebrick\", \"blue\")). show.values Logical, whether values plotted . show.legend Marginal Effects plots, shows hides legend. show.intercept Logical, TRUE, intercept fitted model also plotted. Default FALSE. transform = \"exp\", please note due exponential transformation estimates, intercept cases non-finite plot can created. show.p Logical, adds asterisks indicate significance level estimates value labels. p.shape Logical, TRUE, significant levels distinguished different point shapes related legend plotted. Default FALSE. p.threshold Numeric vector length 3, indicating treshold annotating p-values asterisks. applies p.style = \"asterisk\". p.adjust Character vector, NULL, indicates method adjust p-values. See p.adjust details. ci.lvl Numeric, level confidence intervals (error bars). Use ci.lvl = NA remove error bars. stanreg-models, ci.lvl defines (outer) probability credible interval plotted (see ci). default, stanreg-models printed two intervals: \"inner\" interval, defaults 50%-CI; \"outer\" interval, defaults 89%-CI. ci.lvl affects outer interval cases. See prob.inner prob.outer ...-argument details. robust Deprecated. Please use vcov.fun directly specify estimation variance-covariance matrix. vcov.fun Variance-covariance matrix used compute uncertainty estimates (e.g., robust standard errors). argument accepts covariance matrix, function returns covariance matrix, string identifies function used compute covariance matrix. See model_parameters(). vcov.type Deprecated. type-argument now included vcov.args. vcov.args List arguments passed function identified vcov.fun argument. function typically supplied sandwich clubSandwich packages. Please refer documentation (e.g., ?sandwich::vcovHAC) see list available arguments. vline.color Color vertical \"zero effect\" line. Default color inherited current theme. digits Numeric, amount digits decimal point rounding estimates values. grid Logical, TRUE, multiple plots plotted grid layout. auto.label Logical, TRUE (default),  data labelled,  term_labels called retrieve labels  coefficients, used predictor labels. data  labelled, format_parameters()  used create pretty labels. auto.label = FALSE, original variable names value labels (factor levels) used. prefix.labels Indicates whether value labels categorical variables prefixed, e.g. variable name variable label. See argument prefix term_labels details.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_models.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Forest plot of multiple regression models — plot_models","text":"ggplot-object.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_models.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Forest plot of multiple regression models — plot_models","text":"","code":"data(efc)  # fit three models fit1 <- lm(barthtot ~ c160age + c12hour + c161sex + c172code, data = efc) fit2 <- lm(neg_c_7 ~ c160age + c12hour + c161sex + c172code, data = efc) fit3 <- lm(tot_sc_e ~ c160age + c12hour + c161sex + c172code, data = efc)  # plot multiple models plot_models(fit1, fit2, fit3, grid = TRUE)   # plot multiple models with legend labels and # point shapes instead of value labels plot_models(   fit1, fit2, fit3,   axis.labels = c(     \"Carer's Age\", \"Hours of Care\", \"Carer's Sex\", \"Educational Status\"   ),   m.labels = c(\"Barthel Index\", \"Negative Impact\", \"Services used\"),   show.values = FALSE, show.p = FALSE, p.shape = TRUE )   if (FALSE) { # \\dontrun{ # plot multiple models from nested lists argument all.models <- list() all.models[[1]] <- fit1 all.models[[2]] <- fit2 all.models[[3]] <- fit3  plot_models(all.models)  # plot multiple models with different predictors (stepwise inclusion), # standardized estimates fit1 <- lm(mpg ~ wt + cyl + disp + gear, data = mtcars) fit2 <- update(fit1, . ~ . + hp) fit3 <- update(fit2, . ~ . + am)  plot_models(fit1, fit2, fit3, std.est = \"std2\") } # }"},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_residuals.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot predicted values and their residuals — plot_residuals","title":"Plot predicted values and their residuals — plot_residuals","text":"function plots observed predicted values response              linear (mixed) models coefficient highlights              observed values according distance (residuals)              predicted values. allows investigate well actual              predicted values outcome fit across predictor variables.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_residuals.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot predicted values and their residuals — plot_residuals","text":"","code":"plot_residuals(   fit,   geom.size = 2,   remove.estimates = NULL,   show.lines = TRUE,   show.resid = TRUE,   show.pred = TRUE,   show.ci = FALSE )"},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_residuals.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot predicted values and their residuals — plot_residuals","text":"fit Fitted linear (mixed) regression model (including objects class gls plm). geom.size size resp. width geoms (bar width, line thickness point size, depending plot type function). Note bar bin widths mostly need smaller values dot sizes. remove.estimates Numeric vector indices (order equals row index coef(fit)) character vector coefficient names indicate estimates removed table output. first estimate intercept, followed model predictors. intercept removed table output! remove.estimates = c(2:4) remove 2nd 4th estimate (1st 3rd predictor intercept) output. remove.estimates = \"est_name\" remove estimate est_name. Default NULL, .e. estimates printed. show.lines Logical, TRUE, line connecting predicted residual values plotted. Set argument FALSE, plot-building time consuming. show.resid Logical, TRUE, residual values plotted. show.pred Logical, TRUE, predicted values plotted. show.ci Logical, TRUE), adds notches box plot, used compare groups; notches two boxes overlap, medians considered significantly different.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_residuals.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot predicted values and their residuals — plot_residuals","text":"ggplot-object.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_residuals.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Plot predicted values and their residuals — plot_residuals","text":"actual (observed) values coloured fill, predicted       values solid outline without filling.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_residuals.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot predicted values and their residuals — plot_residuals","text":"","code":"data(efc) # fit model fit <- lm(neg_c_7 ~ c12hour + e17age + e42dep, data = efc)  # plot residuals for all independent variables plot_residuals(fit) #> `geom_smooth()` using formula = 'y ~ x'   # remove some independent variables from output plot_residuals(fit, remove.estimates = c(\"e17age\", \"e42dep\")) #> `geom_smooth()` using formula = 'y ~ x'"},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_scatter.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot (grouped) scatter plots — plot_scatter","title":"Plot (grouped) scatter plots — plot_scatter","text":"Display scatter plot two variables. Adding grouping variable   scatter plot possible. Furthermore, fitted lines can added   group well overall plot.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_scatter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot (grouped) scatter plots — plot_scatter","text":"","code":"plot_scatter(   data,   x,   y,   grp,   title = \"\",   legend.title = NULL,   legend.labels = NULL,   dot.labels = NULL,   axis.titles = NULL,   dot.size = 1.5,   label.size = 3,   colors = \"metro\",   fit.line = NULL,   fit.grps = NULL,   show.rug = FALSE,   show.legend = TRUE,   show.ci = FALSE,   wrap.title = 50,   wrap.legend.title = 20,   wrap.legend.labels = 20,   jitter = 0.05,   emph.dots = FALSE,   grid = FALSE )"},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_scatter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot (grouped) scatter plots — plot_scatter","text":"data data frame, grouped data frame. x Name variable x-axis. y Name variable y-axis. grp Optional, name grouping-variable. missing, scatter plot grouped. See 'Examples'. title Character vector, used plot title. default, response_labels called retrieve label dependent variable, used title. Use title = \"\" remove title. legend.title Character vector, used legend title plots legend. legend.labels character vector labels guide/legend. dot.labels Character vector names coordinate pair given x y, text labels added plot. Must length x y. dot.labels different length, data points trimmed match dot.labels. dot.labels = NULL (default), labels printed. axis.titles character vector length one two, defining title(s) x-axis y-axis. dot.size Numeric, size dots indicate point estimates. label.size Size text labels argument dot.labels used. colors May character vector color values hex-format, valid color value names (see demo(\"colors\")) name pre-defined color palette. Following options valid colors argument: specified, default color brewer palette used, suitable plot style. \"gs\", greyscale used. \"bw\", plot-type line-plot, plot black/white uses different line types distinguish groups (see package-vignette). colors valid color brewer palette name, related palette used. Use RColorBrewer::display.brewer.() view available palette names. pre-defined color palettes package, see sjPlot-themes details. Else specify color values names vector (e.g. colors = \"#00ff00\" colors = c(\"firebrick\", \"blue\")). fit.line, fit.grps Specifies method add fitted line accross data points. Possible values instance \"lm\", \"glm\", \"loess\" \"auto\". NULL, line plotted. fit.line adds fitted line complete data, fit.grps adds fitted line subgroup grp. show.rug Logical, TRUE, marginal rug plot displayed graph. show.legend Marginal Effects plots, shows hides legend. show.ci Logical, TRUE), adds notches box plot, used compare groups; notches two boxes overlap, medians considered significantly different. wrap.title Numeric, determines many chars plot title displayed one line line break inserted. wrap.legend.title numeric, determines many chars legend's title displayed one line line break inserted. wrap.legend.labels numeric, determines many chars legend labels displayed one line line break inserted. jitter Numeric, 0 1. show.data = TRUE, can add small amount random variation location data point. jitter indicates width, .e. much bin's width occupied jittered values. emph.dots Logical, TRUE, overlapping points coordinates becomme larger, point size indicates amount overlapping. grid Logical, TRUE, multiple plots plotted grid layout.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_scatter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot (grouped) scatter plots — plot_scatter","text":"ggplot-object. grouped data frames, list ggplot-objects   group data.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_scatter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot (grouped) scatter plots — plot_scatter","text":"","code":"# load sample date library(sjmisc) library(sjlabelled) data(efc)  # simple scatter plot plot_scatter(efc, e16sex, neg_c_7)   # simple scatter plot, increased jittering plot_scatter(efc, e16sex, neg_c_7, jitter = .4)   # grouped scatter plot plot_scatter(efc, c160age, e17age, e42dep)   # grouped scatter plot with marginal rug plot # and add fitted line for complete data plot_scatter(   efc, c12hour, c160age, c172code,   show.rug = TRUE, fit.line = \"lm\" ) #> `geom_smooth()` using formula = 'y ~ x'   # grouped scatter plot with marginal rug plot # and add fitted line for each group plot_scatter(   efc, c12hour, c160age, c172code,   show.rug = TRUE, fit.grps = \"loess\",   grid = TRUE ) #> `geom_smooth()` using formula = 'y ~ x'"},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_stackfrq.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot stacked proportional bars — plot_stackfrq","title":"Plot stacked proportional bars — plot_stackfrq","text":"Plot items (variables) scale stacked proportional bars.                function useful several items identical scale/categoroies                plotted compare distribution answers.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_stackfrq.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot stacked proportional bars — plot_stackfrq","text":"","code":"plot_stackfrq(   items,   title = NULL,   legend.title = NULL,   legend.labels = NULL,   axis.titles = NULL,   axis.labels = NULL,   weight.by = NULL,   sort.frq = NULL,   wrap.title = 50,   wrap.labels = 30,   wrap.legend.title = 30,   wrap.legend.labels = 28,   geom.size = 0.5,   geom.colors = \"Blues\",   show.prc = TRUE,   show.n = FALSE,   show.total = TRUE,   show.axis.prc = TRUE,   show.legend = TRUE,   grid.breaks = 0.2,   expand.grid = FALSE,   digits = 1,   vjust = \"center\",   coord.flip = TRUE )"},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_stackfrq.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot stacked proportional bars — plot_stackfrq","text":"items Data frame, grouped data frame, column representing one item. title character vector, used plot title. Depending plot type function, set automatically. title = \"\", title printed. effect-plots, may also character vector length > 1, define titles sub-plot facet. legend.title character vector, used title plot legend. legend.labels character vector labels guide/legend. axis.titles character vector length one two, defining title(s) x-axis y-axis. axis.labels character vector labels used axis labels. Optional argument, since cases, axis labels set automatically. weight.Vector weights applied weight cases. Must vector length input vector. Default NULL, weights used. sort.frq Indicates whether items ordered highest count first last category items. \"first.asc\" order ascending lowest count first category, \"first.desc\" order descending lowest count first category, \"last.asc\" order ascending lowest count last category, \"last.desc\" order descending lowest count last category, NULL (default) sorting. wrap.title numeric, determines many chars plot title displayed one line line break inserted. wrap.labels numeric, determines many chars value, variable axis labels displayed one line line break inserted. wrap.legend.title numeric, determines many chars legend's title displayed one line line break inserted. wrap.legend.labels numeric, determines many chars legend labels displayed one line line break inserted. geom.size size resp. width geoms (bar width, line thickness point size, depending plot type function). Note bar bin widths mostly need smaller values dot sizes. geom.colors user defined color geoms. See 'Details' plot_grpfrq. show.prc Logical, whether percentage values plotted . show.n Logical, whether count values hould plotted . show.total logical, TRUE, adds total number cases group category labels. show.axis.prc Logical, TRUE (default), percentage values x-axis shown. show.legend logical, TRUE, depending plot type function, legend added plot. grid.breaks numeric; sets distance breaks axis, .e. every grid.breaks'th position major grid printed. expand.grid logical, TRUE, plot grid expanded, .e. small margin axes plotting region. Default FALSE. digits Numeric, amount digits decimal point rounding estimates values. vjust character vector, indicating vertical position value labels. Allowed values vjust aesthetics ggplot2: \"left\", \"center\", \"right\", \"bottom\", \"middle\", \"top\" new options like \"inward\" \"outward\", align text towards away center plot respectively. coord.flip logical, TRUE, x y axis swapped.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_stackfrq.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot stacked proportional bars — plot_stackfrq","text":"ggplot-object.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_stackfrq.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot stacked proportional bars — plot_stackfrq","text":"","code":"# Data from the EUROFAMCARE sample dataset library(sjmisc) data(efc) # recveive first item of COPE-index scale start <- which(colnames(efc) == \"c82cop1\") # recveive first item of COPE-index scale end <- which(colnames(efc) == \"c90cop9\") # auto-detection of labels plot_stackfrq(efc[, start:end])   # works on grouped data frames as well library(dplyr) efc %>%   group_by(c161sex) %>%   select(start:end) %>%   plot_stackfrq() #> Warning: Using an external vector in selections was deprecated in tidyselect 1.1.0. #> ℹ Please use `all_of()` or `any_of()` instead. #>   # Was: #>   data %>% select(start) #>  #>   # Now: #>   data %>% select(all_of(start)) #>  #> See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>. #> Warning: Using an external vector in selections was deprecated in tidyselect 1.1.0. #> ℹ Please use `all_of()` or `any_of()` instead. #>   # Was: #>   data %>% select(end) #>  #>   # Now: #>   data %>% select(all_of(end)) #>  #> See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>. #> Adding missing grouping variables: `c161sex` #> [[1]] #> [[1]][[1]] #> NULL #>  #> [[1]][[2]]  #>  #>  #> [[2]]  #>"},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_xtab.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot contingency tables — plot_xtab","title":"Plot contingency tables — plot_xtab","text":"Plot proportional crosstables (contingency tables) two variables ggplot diagram.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_xtab.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot contingency tables — plot_xtab","text":"","code":"plot_xtab(   x,   grp,   type = c(\"bar\", \"line\"),   margin = c(\"col\", \"cell\", \"row\"),   bar.pos = c(\"dodge\", \"stack\"),   title = \"\",   title.wtd.suffix = NULL,   axis.titles = NULL,   axis.labels = NULL,   legend.title = NULL,   legend.labels = NULL,   weight.by = NULL,   rev.order = FALSE,   show.values = TRUE,   show.n = TRUE,   show.prc = TRUE,   show.total = TRUE,   show.legend = TRUE,   show.summary = FALSE,   summary.pos = \"r\",   drop.empty = TRUE,   string.total = \"Total\",   wrap.title = 50,   wrap.labels = 15,   wrap.legend.title = 20,   wrap.legend.labels = 20,   geom.size = 0.7,   geom.spacing = 0.1,   geom.colors = \"Paired\",   dot.size = 3,   smooth.lines = FALSE,   grid.breaks = 0.2,   expand.grid = FALSE,   ylim = NULL,   vjust = \"bottom\",   hjust = \"center\",   y.offset = NULL,   coord.flip = FALSE )"},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_xtab.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot contingency tables — plot_xtab","text":"x vector values (variable) describing bars make plot. grp Grouping variable length x, x grouped categories represented grp. type Plot type. may either \"bar\" (default) bar charts, \"line\" line diagram. margin Indicates data proportional table plotted. Use \"row\" calculating row percentages, \"col\" column percentages \"cell\" cell percentages. margin = \"col\", additional bar total sum column can added plot (see show.total). bar.pos Indicates whether bars positioned side--side (default), stacked (bar.pos = \"stack\"). May abbreviated. title character vector, used plot title. Depending plot type function, set automatically. title = \"\", title printed. effect-plots, may also character vector length > 1, define titles sub-plot facet. title.wtd.suffix Suffix (string) title, weight.specified, e.g. title.wtd.suffix=\" (weighted)\". Default NULL, title suffix cases weighted. axis.titles character vector length one two, defining title(s) x-axis y-axis. axis.labels character vector labels used axis labels. Optional argument, since cases, axis labels set automatically. legend.title character vector, used title plot legend. legend.labels character vector labels guide/legend. weight.Vector weights applied weight cases. Must vector length input vector. Default NULL, weights used. rev.order Logical, TRUE, order categories (groups) reversed. show.values Logical, whether values plotted . show.n logical, TRUE, adds total number cases group category labels. show.prc logical, TRUE (default), percentage values plotted bar FALSE, percentage values removed. show.total margin = \"col\", additional bar sum within category percentages added category. show.legend logical, TRUE, depending plot type function, legend added plot. show.summary logical, TRUE (default), summary chi-squared statistics (see chisq.test), Cramer's V Phi-value etc. shown. cell contains expected values lower five (lower 10 df 1), Fisher's exact test (see fisher.test) computed instead chi-squared test. table's matrix larger 2x2, Fisher's exact test Monte Carlo simulation computed. summary.pos position model summary printed show.summary TRUE. Default \"r\", .e. printed upper right corner. Use \"l\" upper left corner. drop.empty Logical, TRUE variable's values labeled, values / factor levels occurrence data omitted output. FALSE, labeled values observations still printed table (frequency 0). string.total String legend label total-column added. applies show.total = TRUE. Default \"Total\". wrap.title numeric, determines many chars plot title displayed one line line break inserted. wrap.labels numeric, determines many chars value, variable axis labels displayed one line line break inserted. wrap.legend.title numeric, determines many chars legend's title displayed one line line break inserted. wrap.legend.labels numeric, determines many chars legend labels displayed one line line break inserted. geom.size size resp. width geoms (bar width, line thickness point size, depending plot type function). Note bar bin widths mostly need smaller values dot sizes. geom.spacing spacing geoms (.e. bar spacing) geom.colors user defined color geoms. See 'Details' plot_grpfrq. dot.size Dot size, applies, argument type = \"line\". smooth.lines prints smooth line curve. applies, argument type = \"line\". grid.breaks numeric; sets distance breaks axis, .e. every grid.breaks'th position major grid printed. expand.grid logical, TRUE, plot grid expanded, .e. small margin axes plotting region. Default FALSE. ylim numeric vector length two, defining lower upper axis limits y scale. default, argument set NULL, .e. y-axis fits required range data. vjust character vector, indicating vertical position value labels. Allowed values vjust aesthetics ggplot2: \"left\", \"center\", \"right\", \"bottom\", \"middle\", \"top\" new options like \"inward\" \"outward\", align text towards away center plot respectively. hjust character vector, indicating horizontal position value labels. Allowed values vjust aesthetics ggplot2: \"left\", \"center\", \"right\", \"bottom\", \"middle\", \"top\" new options like \"inward\" \"outward\", align text towards away center plot respectively. y.offset numeric, offset text labels alignment adjusted top/bottom geom (see hjust vjust). coord.flip logical, TRUE, x y axis swapped.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_xtab.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot contingency tables — plot_xtab","text":"ggplot-object.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_xtab.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot contingency tables — plot_xtab","text":"","code":"# create 4-category-items grp <- sample(1:4, 100, replace = TRUE) # create 3-category-items x <- sample(1:3, 100, replace = TRUE)  # plot \"cross tablulation\" of x and grp plot_xtab(x, grp)   # plot \"cross tablulation\" of x and y, including labels plot_xtab(x, grp, axis.labels = c(\"low\", \"mid\", \"high\"),          legend.labels = c(\"Grp 1\", \"Grp 2\", \"Grp 3\", \"Grp 4\"))   # plot \"cross tablulation\" of x and grp # as stacked proportional bars plot_xtab(x, grp, margin = \"row\", bar.pos = \"stack\",          show.summary = TRUE, coord.flip = TRUE) #> Warning: Chi-squared approximation may be incorrect   # example with vertical labels library(sjmisc) library(sjlabelled) data(efc) set_theme(geom.label.angle = 90) plot_xtab(efc$e42dep, efc$e16sex, vjust = \"center\", hjust = \"bottom\")   # grouped bars with EUROFAMCARE sample dataset # dataset was importet from an SPSS-file, # see ?sjmisc::read_spss data(efc) efc.val <- get_labels(efc) efc.var <- get_label(efc)  plot_xtab(efc$e42dep, efc$e16sex, title = efc.var['e42dep'],          axis.labels = efc.val[['e42dep']], legend.title = efc.var['e16sex'],          legend.labels = efc.val[['e16sex']])   plot_xtab(efc$e16sex, efc$e42dep, title = efc.var['e16sex'],          axis.labels = efc.val[['e16sex']], legend.title = efc.var['e42dep'],          legend.labels = efc.val[['e42dep']])   # ------------------------------- # auto-detection of labels works here # so no need to specify labels. For # title-auto-detection, use NULL # ------------------------------- plot_xtab(efc$e16sex, efc$e42dep, title = NULL)   plot_xtab(efc$e16sex, efc$e42dep, margin = \"row\",          bar.pos = \"stack\", coord.flip = TRUE)"},{"path":"https://strengejacke.github.io/sjPlot/reference/save_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Save ggplot-figure for print publication — save_plot","title":"Save ggplot-figure for print publication — save_plot","text":"Convenient function save last ggplot-figure                high quality publication.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/save_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Save ggplot-figure for print publication — save_plot","text":"","code":"save_plot(   filename,   fig = last_plot(),   width = 12,   height = 9,   dpi = 300,   theme = theme_get(),   label.color = \"black\",   label.size = 2.4,   axis.textsize = 0.8,   axis.titlesize = 0.75,   legend.textsize = 0.6,   legend.titlesize = 0.65,   legend.itemsize = 0.5 )"},{"path":"https://strengejacke.github.io/sjPlot/reference/save_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Save ggplot-figure for print publication — save_plot","text":"filename Name output file; filename must end one following accepted file types: \".png\", \".jpg\", \".svg\" \".tif\". fig plot saved. default, last plot saved. width Width figure, centimetres. height Height figure, centimetres. dpi Resolution dpi (dots per inch). Ignored vector formats, \".svg\". theme default theme use saving plot. label.color Color value labels (axis, plot, etc.). label.size Fontsize value labels inside plot area. axis.textsize Fontsize axis labels. axis.titlesize Fontsize axis titles. legend.textsize Fontsize legend labels. legend.titlesize Fontsize legend title. legend.itemsize Size legend's item (legend key), centimetres.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/save_plot.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Save ggplot-figure for print publication — save_plot","text":"convenient function default settings         come close needs fontsize scaling figures         saving printing publishing. uses cairographics         anti-aliasing (see png).                  adjusting plot appearance, see also sjPlot-themes.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/set_theme.html","id":null,"dir":"Reference","previous_headings":"","what":"Set global theme options for sjp-functions — set_theme","title":"Set global theme options for sjp-functions — set_theme","text":"Set global theme options sjp-functions.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/set_theme.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set global theme options for sjp-functions — set_theme","text":"","code":"set_theme(   base = theme_grey(),   theme.font = NULL,   title.color = \"black\",   title.size = 1.2,   title.align = \"left\",   title.vjust = NULL,   geom.outline.color = NULL,   geom.outline.size = 0,   geom.boxoutline.size = 0.5,   geom.boxoutline.color = \"black\",   geom.alpha = 1,   geom.linetype = 1,   geom.errorbar.size = 0.7,   geom.errorbar.linetype = 1,   geom.label.color = NULL,   geom.label.size = 4,   geom.label.alpha = 1,   geom.label.angle = 0,   axis.title.color = \"grey30\",   axis.title.size = 1.1,   axis.title.x.vjust = NULL,   axis.title.y.vjust = NULL,   axis.angle.x = 0,   axis.angle.y = 0,   axis.angle = NULL,   axis.textcolor.x = \"grey30\",   axis.textcolor.y = \"grey30\",   axis.textcolor = NULL,   axis.linecolor.x = NULL,   axis.linecolor.y = NULL,   axis.linecolor = NULL,   axis.line.size = 0.5,   axis.textsize.x = 1,   axis.textsize.y = 1,   axis.textsize = NULL,   axis.tickslen = NULL,   axis.tickscol = NULL,   axis.ticksmar = NULL,   axis.ticksize.x = NULL,   axis.ticksize.y = NULL,   panel.backcol = NULL,   panel.bordercol = NULL,   panel.col = NULL,   panel.major.gridcol = NULL,   panel.minor.gridcol = NULL,   panel.gridcol = NULL,   panel.gridcol.x = NULL,   panel.gridcol.y = NULL,   panel.major.linetype = 1,   panel.minor.linetype = 1,   plot.backcol = NULL,   plot.bordercol = NULL,   plot.col = NULL,   plot.margins = NULL,   legend.pos = \"right\",   legend.just = NULL,   legend.inside = FALSE,   legend.size = 1,   legend.color = \"black\",   legend.title.size = 1,   legend.title.color = \"black\",   legend.title.face = \"bold\",   legend.backgroundcol = \"white\",   legend.bordercol = \"white\",   legend.item.size = NULL,   legend.item.backcol = \"grey90\",   legend.item.bordercol = \"white\" )"},{"path":"https://strengejacke.github.io/sjPlot/reference/set_theme.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set global theme options for sjp-functions — set_theme","text":"base base theme theme built . default, metrics theme_gray() used. See 'Details'. theme.font base font family plot. title.color Color plot title. Default \"black\". title.size size plot title. Default 1.3. title.align alignment plot title. Must one \"left\" (default), \"center\" \"right\". may use initial letter . title.vjust numeric, vertical adjustment plot title. geom.outline.color Color geom outline. applies, geom.outline.size larger 0. geom.outline.size size bar outlines. Default 0.1. Use size 0 remove geom outline. geom.boxoutline.size size outlines median bar especially boxplots. Default 0.5. Use size 0 remove boxplot outline. geom.boxoutline.color Color outlines median bar especially boxplots. applies, geom.boxoutline.size larger 0. geom.alpha specifies transparancy (alpha value) geoms geom.linetype linetype line geoms. Default 1 (solid line). geom.errorbar.size size (thickness) error bars. Default 0.8 geom.errorbar.linetype linetype error bars. Default 1 (solid line). geom.label.color Color geom's value annotation labels geom.label.size size geom's value annotation labels geom.label.alpha alpha level geom's value annotation labels geom.label.angle angle geom's value annotation labels axis.title.color Color x- y-axis title labels axis.title.size size x- y-axis title labels axis.title.x.vjust numeric, vertical adjustment x-axis-title. axis.title.y.vjust numeric, vertical adjustment y-axis-title. axis.angle.x angle x-axis labels axis.angle.y angle y-axis labels axis.angle angle x- y-axis labels. set, overrides axis.angle.x axis.angle.y axis.textcolor.x Color x-axis labels. specified, default dark gray color palette used labels. axis.textcolor.y Color y-axis labels. specified, default dark gray color palette used labels. axis.textcolor Color x- y-axis labels. set, overrides axis.textcolor.x axis.textcolor.y axis.linecolor.x Color x-axis border axis.linecolor.y Color y-axis border axis.linecolor Color x- y-axis borders. set, overrides axis.linecolor.x axis.linecolor.y. axis.line.size size (thickness) axis lines. affected, axis.linecolor set. axis.textsize.x size x-axis labels axis.textsize.y size y-axis labels axis.textsize size x- y-axis labels. set, overrides axis.textsize.x axis.textsize.y. axis.tickslen length axis tick marks axis.tickscol Color axis tick marks axis.ticksmar margin axis labels tick marks axis.ticksize.x size tick marks x-axis. axis.ticksize.y size tick marks y-axis. panel.backcol Color diagram's background panel.bordercol Color whole diagram border (panel border) panel.col Color diagram's border background. set, overrides panel.bordercol panel.backcol. panel.major.gridcol Color major grid lines diagram background panel.minor.gridcol Color minor grid lines diagram background panel.gridcol Color minor major grid lines diagram background. set, overrides panel.major.gridcol panel.minor.gridcol. panel.gridcol.x See panel.gridcol. panel.gridcol.y See panel.gridcol. panel.major.linetype line type major grid lines panel.minor.linetype line type minor grid lines plot.backcol Color plot's background plot.bordercol Color whole plot's border (panel border) plot.col Color plot's region border background. set, overrides plot.backcol plot.bordercol. plot.margins numeric vector length 4, indicating top, right, bottom left margin plot region. legend.pos position legend, legend drawn. legend outside plot Use \"bottom\", \"top\", \"left\" \"right\"     position legend , , left right side     diagram. Right positioning default. legend inside plot legend.inside = TRUE, legend can placed inside     plot. Use \"top left\", \"top right\", \"bottom left\"     \"bottom right\" position legend corners,     two-element numeric vector values 0-1. See also     legend.inside. legend.just justification legend, relative position (\"center\" two-element numeric vector values 0-1. default (outside legend), justification centered. legend inside justification specified, legend justification set according legend position. legend.inside logical, use TRUE put legend inside plotting area. See legend.pos. legend.size text size legend. Default 1. Relative size, recommended values 0.3 2.5 legend.color Color legend labels legend.title.size text size legend title legend.title.color Color legend title legend.title.face font face legend title. default, \"bold\" face used. legend.backgroundcol fill color legend's background. Default \"white\", visible background drawn. legend.bordercol Color legend's border. Default \"white\", visible border drawn. legend.item.size size legend's item (legend key), centimetres. legend.item.backcol fill color legend's item-background. Default \"grey90\". legend.item.bordercol Color legend's item-border. Default \"white\".","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/set_theme.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Set global theme options for sjp-functions — set_theme","text":"customized theme object, NULL, ggplot-theme used.","code":""},{"path":[]},{"path":"https://strengejacke.github.io/sjPlot/reference/set_theme.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Set global theme options for sjp-functions — set_theme","text":"","code":"if (FALSE) { # \\dontrun{ library(sjmisc) data(efc) # set sjPlot-defaults, a slightly modification # of the ggplot base theme set_theme()  # legends of all plots inside set_theme(legend.pos = \"top left\", legend.inside = TRUE) plot_xtab(efc$e42dep, efc$e16sex)  # Use classic-theme. you may need to # load the ggplot2-library. library(ggplot2) set_theme(base = theme_classic()) plot_frq(efc$e42dep)  # adjust value labels set_theme(   geom.label.size = 3.5,   geom.label.color = \"#3366cc\",   geom.label.angle = 90 )  # hjust-aes needs adjustment for this update_geom_defaults('text', list(hjust = -0.1)) plot_xtab(efc$e42dep, efc$e16sex, vjust = \"center\", hjust = \"center\")  # Create own theme based on classic-theme set_theme(   base = theme_classic(), axis.linecolor = \"grey50\",   axis.textcolor = \"#6699cc\" ) plot_frq(efc$e42dep)} # }"},{"path":"https://strengejacke.github.io/sjPlot/reference/sjPlot-package.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Visualization for Statistics in Social Science — sjPlot-package","title":"Data Visualization for Statistics in Social Science — sjPlot-package","text":"Collection plotting table output functions data visualization. Results various statistical analyses (commonly used social sciences) can visualized using package, including simple cross tabulated frequencies, histograms, box plots, (generalized) linear models, mixed effects models, PCA correlation matrices, cluster analyses, scatter plots, Likert scales, effects plots interaction terms regression models, constructing index score variables much . package supports labelled data, .e. value variable labels labelled data (like vectors data frames) automatically used label output. labels can specified well. package ? short, functions package mostly two things: compute basic advanced statistical analyses either plot results ggplot-figure print html-table package help ? One challenging tasks working R get nicely formatted output statistical analyses, either graphical table format. sjPlot-package takes tasks makes easy create beautiful figures tables. many examples function related help files comprehensive online documentation https://strengejacke.github.io/sjPlot/. note package functions main functions follow specific naming conventions, hence starting specific prefix, indicates kind task functions perform. sjc - cluster analysis functions sjp - plotting functions sjt - (HTML) table output functions","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/sjPlot-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Data Visualization for Statistics in Social Science — sjPlot-package","text":"Daniel Lüdecke d.luedecke@uke.de","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/sjPlot-themes.html","id":null,"dir":"Reference","previous_headings":"","what":"Modify plot appearance — sjPlot-themes","title":"Modify plot appearance — sjPlot-themes","text":"Set default plot themes, use pre-defined color scales modify   plot table appearance.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/sjPlot-themes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Modify plot appearance — sjPlot-themes","text":"","code":"theme_sjplot(base_size = 12, base_family = \"\")  theme_sjplot2(base_size = 12, base_family = \"\")  theme_blank(base_size = 12, base_family = \"\")  theme_538(base_size = 12, base_family = \"\")  font_size(   title,   axis_title.x,   axis_title.y,   labels.x,   labels.y,   offset.x,   offset.y,   base.theme )  label_angle(angle.x, angle.y, base.theme)  legend_style(inside, pos, justify, base.theme)  scale_color_sjplot(palette = \"metro\", discrete = TRUE, reverse = FALSE, ...)  scale_fill_sjplot(palette = \"metro\", discrete = TRUE, reverse = FALSE, ...)  sjplot_pal(palette = \"metro\", n = NULL)  show_sjplot_pals()  css_theme(css.theme = \"regression\")"},{"path":"https://strengejacke.github.io/sjPlot/reference/sjPlot-themes.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Modify plot appearance — sjPlot-themes","text":"base_size Base font size. base_family Base font family. title Font size plot titles. axis_title.x Font size x-axis titles. axis_title.y Font size y-axis titles. labels.x Font size x-axis labels. labels.y Font size y-axis labels. offset.x Offset x-axis titles. offset.y Offset y-axis titles. base.theme Optional ggplot-theme-object, needed case multiple functions combined, e.g. theme_sjplot() + label_angle(). cases, use label_angle(base.theme = theme_sjplot()). angle.x Angle x-axis labels. angle.y Angle y-axis labels. inside Logical, use TRUE put legend inside plotting area. See also pos. pos Position legend, legend drawn. Legend outside plot Use \"bottom\", \"top\", \"left\" \"right\"     position legend , , left right side     diagram. Legend inside plot inside = TRUE, legend can placed inside     plot. Use \"top left\", \"top right\", \"bottom left\"     \"bottom right\" position legend corners,     two-element numeric vector values 0-1. See also     inside. justify Justification legend, relative position (\"center\" two-element numeric vector values 0-1. palette Character name color palette. discrete Logical, TRUE, discrete colour palette returned. Else, gradient palette returned, colours requested palette interpolated using colorRampPalette. reverse Logical, TRUE, order returned colours reversed. ... arguments passed ggplot's scale()-functions. n Numeric, number colors returned. default, complete colour palette returned. css.theme Name CSS pre-set theme-style. Can used table-functions.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/sjPlot-themes.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Modify plot appearance — sjPlot-themes","text":"using colors argument function calls (e.g.   plot_model()) calling one predefined scale-functions   (e.g. scale_color_sjplot()), pre-defined colour palettes   package. Use show_sjplot_pals() show available   colour palettes.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/sjPlot-themes.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Modify plot appearance — sjPlot-themes","text":"","code":"# prepare data if (requireNamespace(\"haven\")) { library(sjmisc) data(efc) efc <- to_factor(efc, c161sex, e42dep, c172code) m <- lm(neg_c_7 ~ pos_v_4 + c12hour + e42dep + c172code, data = efc)  # create plot-object p <- plot_model(m)  # change theme p + theme_sjplot()  # change font-size p + font_size(axis_title.x = 30)  # apply color theme p + scale_color_sjplot()  # show all available colour palettes show_sjplot_pals()  # get colour values from specific palette sjplot_pal(pal = \"breakfast club\") } #> Scale for colour is already present. #> Adding another scale for colour, which will replace the existing scale. #> [1] \"#b6411a\" \"#4182dd\" \"#2d6328\" \"#eec3d8\" \"#ecf0c8\""},{"path":"https://strengejacke.github.io/sjPlot/reference/sjp.aov1.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot One-Way-Anova tables — sjp.aov1","title":"Plot One-Way-Anova tables — sjp.aov1","text":"Plot One-Way-Anova table sum squares (SS) factor level (group)                dependent variable. SS factor variable                dependent variable (variance within groups) printed                model summary.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/sjp.aov1.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot One-Way-Anova tables — sjp.aov1","text":"","code":"sjp.aov1(   var.dep,   var.grp,   meansums = FALSE,   title = NULL,   axis.labels = NULL,   rev.order = FALSE,   string.interc = \"(Intercept)\",   axis.title = \"\",   axis.lim = NULL,   geom.colors = c(\"#3366a0\", \"#aa3333\"),   geom.size = 3,   wrap.title = 50,   wrap.labels = 25,   grid.breaks = NULL,   show.values = TRUE,   digits = 2,   y.offset = 0.15,   show.p = TRUE,   show.summary = FALSE )"},{"path":"https://strengejacke.github.io/sjPlot/reference/sjp.aov1.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot One-Way-Anova tables — sjp.aov1","text":"var.dep Dependent variable. used following formula: aov(var.dep ~ var.grp) var.grp Factor cross-classifying variable, var.dep grouped categories represented var.grp. meansums Logical, TRUE, values reported true group mean values. FALSE (default), values reported standard way, .e. values indicate difference group mean relation intercept (reference group). title character vector, used plot title. Depending plot type function, set automatically. title = \"\", title printed. effect-plots, may also character vector length > 1, define titles sub-plot facet. axis.labels character vector labels used axis labels. Optional argument, since cases, axis labels set automatically. rev.order Logical, TRUE, order categories (groups) reversed. string.interc Character vector indicates reference group (intercept), appended value label grouping variable. Default \"(Intercept)\". axis.title Character vector length one two (depending plot function type), used title(s) x y axis. specified, default labelling  chosen. Note: plot types may support argument sufficiently. cases, use returned ggplot-object add axis titles manually labs. Use axis.title = \"\" remove axis titles. axis.lim Numeric vector length 2, defining range plot axis. Depending plot type, may effect either x- y-axis, . multiple plot outputs (e.g., type = \"eff\" type = \"slope\" plot_model), axis.lim may also list vectors length 2, defining axis limits plot (non-faceted). geom.colors user defined color geoms. See 'Details' plot_grpfrq. geom.size size resp. width geoms (bar width, line thickness point size, depending plot type function). Note bar bin widths mostly need smaller values dot sizes. wrap.title numeric, determines many chars plot title displayed one line line break inserted. wrap.labels numeric, determines many chars value, variable axis labels displayed one line line break inserted. grid.breaks numeric; sets distance breaks axis, .e. every grid.breaks'th position major grid printed. show.values Logical, whether values plotted . digits Numeric, amount digits decimal point rounding estimates values. y.offset numeric, offset text labels alignment adjusted top/bottom geom (see hjust vjust). show.p Logical, adds significance levels values, value variable labels. show.summary logical, TRUE (default), summary chi-squared statistics (see chisq.test), Cramer's V Phi-value etc. shown. cell contains expected values lower five (lower 10 df 1), Fisher's exact test (see fisher.test) computed instead chi-squared test. table's matrix larger 2x2, Fisher's exact test Monte Carlo simulation computed.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/sjp.aov1.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot One-Way-Anova tables — sjp.aov1","text":"ggplot-object.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/sjp.aov1.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot One-Way-Anova tables — sjp.aov1","text":"","code":"data(efc) # note: \"var.grp\" does not need to be a factor. # coercion to factor is done by the function sjp.aov1(efc$c12hour, efc$e42dep) #> Warning: Continuous limits supplied to discrete scale. #> ℹ Did you mean `limits = factor(...)` or `scale_*_continuous()`?"},{"path":"https://strengejacke.github.io/sjPlot/reference/sjp.chi2.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot Pearson's Chi2-Test of multiple contingency tables — sjp.chi2","title":"Plot Pearson's Chi2-Test of multiple contingency tables — sjp.chi2","text":"Plot p-values Pearson's Chi2-tests multiple contingency tables ellipses tiles.                Requires data frame dichotomous (dummy) variables.                Calculation Chi2-matrix taken                Tales R.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/sjp.chi2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot Pearson's Chi2-Test of multiple contingency tables — sjp.chi2","text":"","code":"sjp.chi2(   df,   title = \"Pearson's Chi2-Test of Independence\",   axis.labels = NULL,   wrap.title = 50,   wrap.labels = 20,   show.legend = FALSE,   legend.title = NULL )"},{"path":"https://strengejacke.github.io/sjPlot/reference/sjp.chi2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot Pearson's Chi2-Test of multiple contingency tables — sjp.chi2","text":"df data frame (dichotomous) factor variables. title character vector, used plot title. Depending plot type function, set automatically. title = \"\", title printed. effect-plots, may also character vector length > 1, define titles sub-plot facet. axis.labels character vector labels used axis labels. Optional argument, since cases, axis labels set automatically. wrap.title numeric, determines many chars plot title displayed one line line break inserted. wrap.labels numeric, determines many chars value, variable axis labels displayed one line line break inserted. show.legend logical, TRUE, depending plot type function, legend added plot. legend.title character vector, used title plot legend.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/sjp.chi2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot Pearson's Chi2-Test of multiple contingency tables — sjp.chi2","text":"ggplot-object.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/sjp.chi2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot Pearson's Chi2-Test of multiple contingency tables — sjp.chi2","text":"","code":"# create data frame with 5 dichotomous (dummy) variables mydf <- data.frame(as.factor(sample(1:2, 100, replace=TRUE)),                    as.factor(sample(1:2, 100, replace=TRUE)),                    as.factor(sample(1:2, 100, replace=TRUE)),                    as.factor(sample(1:2, 100, replace=TRUE)),                    as.factor(sample(1:2, 100, replace=TRUE))) # create variable labels items <- list(c(\"Item 1\", \"Item 2\", \"Item 3\", \"Item 4\", \"Item 5\"))  # plot Chi2-contingency-table sjp.chi2(mydf, axis.labels = items)"},{"path":"https://strengejacke.github.io/sjPlot/reference/sjp.corr.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot correlation matrix — sjp.corr","title":"Plot correlation matrix — sjp.corr","text":"Plot correlation matrix ellipses tiles.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/sjp.corr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot correlation matrix — sjp.corr","text":"","code":"sjp.corr(   data,   title = NULL,   axis.labels = NULL,   sort.corr = TRUE,   decimals = 3,   na.deletion = c(\"listwise\", \"pairwise\"),   corr.method = c(\"pearson\", \"spearman\", \"kendall\"),   geom.colors = \"RdBu\",   wrap.title = 50,   wrap.labels = 20,   show.legend = FALSE,   legend.title = NULL,   show.values = TRUE,   show.p = TRUE,   p.numeric = FALSE )"},{"path":"https://strengejacke.github.io/sjPlot/reference/sjp.corr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot correlation matrix — sjp.corr","text":"data Matrix correlation coefficients returned cor-function, data.frame variables correlations columns computed. title character vector, used plot title. Depending plot type function, set automatically. title = \"\", title printed. effect-plots, may also character vector length > 1, define titles sub-plot facet. axis.labels character vector labels used axis labels. Optional argument, since cases, axis labels set automatically. sort.corr Logical, TRUE (default), axis labels sorted according correlation strength. FALSE, axis labels appear order variables included cor-computation data frame. decimals Indicates many decimal values comma printed values labels shown. Default 3. applies show.values = TRUE. na.deletion Indicates missing values treated. May either \"listwise\" (default) \"pairwise\". May abbreviated. corr.method Indicates correlation computation method. May one \"pearson\" (default), \"spearman\" \"kendall\". May abbreviated. geom.colors user defined color geoms. See 'Details' plot_grpfrq. wrap.title numeric, determines many chars plot title displayed one line line break inserted. wrap.labels numeric, determines many chars value, variable axis labels displayed one line line break inserted. show.legend logical, TRUE, depending plot type function, legend added plot. legend.title character vector, used title plot legend. show.values Logical, whether values plotted . show.p Logical, adds significance levels values, value variable labels. p.numeric Logical, TRUE, p-values printed numbers. FALSE (default), asterisks used.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/sjp.corr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot correlation matrix — sjp.corr","text":"(Insisibily) returns ggplot-object complete plot (plot) well data frame           used setting ggplot-object (df) original correlation matrix           (corr.matrix).","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/sjp.corr.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plot correlation matrix — sjp.corr","text":"Required argument either data.frame matrix correlation coefficients            returned cor-function. case ellipses,            ellipses size indicates strength correlation. Furthermore,            blue red colors indicate positive negative correlations,            stronger correlations darker.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/sjp.corr.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Plot correlation matrix — sjp.corr","text":"data matrix correlation coefficients returned       cor-function, p-values computed.       Thus, show.p p.numeric       effect data data.frame.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/sjp.poly.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot polynomials for (generalized) linear regression — sjp.poly","title":"Plot polynomials for (generalized) linear regression — sjp.poly","text":"function plots scatter plot term poly.term                response variable x adds - depending                amount numeric values poly.degree - multiple                polynomial curves. loess-smoothed line can added see                polynomial curves fits best data.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/sjp.poly.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot polynomials for (generalized) linear regression — sjp.poly","text":"","code":"sjp.poly(   x,   poly.term,   poly.degree,   poly.scale = FALSE,   fun = NULL,   axis.title = NULL,   geom.colors = NULL,   geom.size = 0.8,   show.loess = TRUE,   show.loess.ci = TRUE,   show.p = TRUE,   show.scatter = TRUE,   point.alpha = 0.2,   point.color = \"#404040\",   loess.color = \"#808080\" )"},{"path":"https://strengejacke.github.io/sjPlot/reference/sjp.poly.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot polynomials for (generalized) linear regression — sjp.poly","text":"x vector, representing response variable linear (mixed) model; linear (mixed) model returned lm lmer. poly.term x vector, poly.term also vector, representing polynomial term (independent variabl) model; x fitted model, poly.term polynomial term's name character string. See 'Examples'. poly.degree Numeric, numeric vector, indicating degree polynomial. poly.degree numeric vector, multiple polynomial curves degree plotted. See 'Examples'. poly.scale Logical, TRUE, poly.term scaled linear regression computed. Default FALSE. Scaling polynomial term may impact resulting p-values. fun Linear function modelling polynomial terms. Use fun = \"lm\" linear models, fun = \"glm\" generalized linear models. x vector, fitted model object, function detected automatically. x vector, fun defaults \"lm\". axis.title Character vector length one two (depending plot function type), used title(s) x y axis. specified, default labelling  chosen. Note: plot types may support argument sufficiently. cases, use returned ggplot-object add axis titles manually labs. Use axis.title = \"\" remove axis titles. geom.colors user defined color geoms. See 'Details' plot_grpfrq. geom.size size resp. width geoms (bar width, line thickness point size, depending plot type function). Note bar bin widths mostly need smaller values dot sizes. show.loess Logical, TRUE, additional loess-smoothed line plotted. show.loess.ci Logical, TRUE, confidence region loess-smoothed line plotted. show.p Logical, TRUE (default), p-values polynomial terms printed console. show.scatter Logical, TRUE (default), adds scatter plot data points plot. point.alpha Alpha value point-geoms scatter plots. applies, show.scatter = TRUE. point.color Color point-geoms scatter plots. applies, show.scatter = TRUE. loess.color Color loess-smoothed line. applies, show.loess = TRUE.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/sjp.poly.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot polynomials for (generalized) linear regression — sjp.poly","text":"ggplot-object.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/sjp.poly.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plot polynomials for (generalized) linear regression — sjp.poly","text":"polynomial degree, simple linear regression x (resp.            extracted response, x fitted model) performed,            polynomial term poly.term included independent variable.            Thus, lm(y ~ x + (x^2) + ... + (x^)) repeatedly computed            values poly.degree, predicted values            reponse plotted raw values poly.term.            x fitted model, covariates ignored            finding best fitting polynomial.             function evaluates raw polynomials, orthogonal polynomials.            Polynomials computed using poly function,            argument raw = TRUE.             find polynomial degree fits best data, loess-smoothed            line (dark grey) can added (show.loess = TRUE). polynomial curves            comes closest loess-smoothed line best            fit data.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/sjp.poly.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot polynomials for (generalized) linear regression — sjp.poly","text":"","code":"library(sjmisc) data(efc) # linear fit. loess-smoothed line indicates a more # or less cubic curve sjp.poly(efc$c160age, efc$quol_5, 1) #> Polynomial degrees: 1 #> --------------------- #> p(x^1): 0.000 #>  #> `geom_smooth()` using formula = 'y ~ x'   # quadratic fit sjp.poly(efc$c160age, efc$quol_5, 2) #> Polynomial degrees: 2 #> --------------------- #> p(x^1): 0.078 #> p(x^2): 0.533 #>  #> `geom_smooth()` using formula = 'y ~ x'   # linear to cubic fit sjp.poly(efc$c160age, efc$quol_5, 1:4, show.scatter = FALSE) #> Polynomial degrees: 1 #> --------------------- #> p(x^1): 0.000 #>  #> Polynomial degrees: 2 #> --------------------- #> p(x^1): 0.078 #> p(x^2): 0.533 #>  #> Polynomial degrees: 3 #> --------------------- #> p(x^1): 0.012 #> p(x^2): 0.001 #> p(x^3): 0.000 #>  #> Polynomial degrees: 4 #> --------------------- #> p(x^1): 0.777 #> p(x^2): 0.913 #> p(x^3): 0.505 #> p(x^4): 0.254 #>  #> `geom_smooth()` using formula = 'y ~ x'    # fit sample model fit <- lm(tot_sc_e ~ c12hour + e17age + e42dep, data = efc) # inspect relationship between predictors and response plot_model(fit, type = \"slope\") #> `geom_smooth()` using formula = 'y ~ x' #> `geom_smooth()` using formula = 'y ~ x' #> Warning: pseudoinverse used at 4.015 #> Warning: neighborhood radius 2.015 #> Warning: reciprocal condition number  1.5462e-16 #> Warning: There are other near singularities as well. 1  # \"e17age\" does not seem to be linear correlated to response # try to find appropiate polynomial. Grey line (loess smoothed) # indicates best fit. Looks like x^4 has the best fit, # however, only x^3 has significant p-values. sjp.poly(fit, \"e17age\", 2:4, show.scatter = FALSE) #> Polynomial degrees: 2 #> --------------------- #> p(x^1): 0.734 #> p(x^2): 0.721 #>  #> Polynomial degrees: 3 #> --------------------- #> p(x^1): 0.010 #> p(x^2): 0.011 #> p(x^3): 0.011 #>  #> Polynomial degrees: 4 #> --------------------- #> p(x^1): 0.234 #> p(x^2): 0.267 #> p(x^3): 0.303 #> p(x^4): 0.343 #>  #> `geom_smooth()` using formula = 'y ~ x'   if (FALSE) { # \\dontrun{ # fit new model fit <- lm(tot_sc_e ~ c12hour + e42dep + e17age + I(e17age^2) + I(e17age^3),           data = efc) # plot marginal effects of polynomial term plot_model(fit, type = \"pred\", terms = \"e17age\")} # }"},{"path":"https://strengejacke.github.io/sjPlot/reference/sjplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Wrapper to create plots and tables within a pipe-workflow — sjplot","title":"Wrapper to create plots and tables within a pipe-workflow — sjplot","text":"function pipe-friendly argument-structure,              first argument always data, followed variables              plotted printed table. function transforms              input calls requested sjp.- resp. sjt.-function              create plot table.               sjplot() sjtab() support grouped data frames.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/sjplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Wrapper to create plots and tables within a pipe-workflow — sjplot","text":"","code":"sjplot(data, ..., fun = c(\"grpfrq\", \"xtab\", \"aov1\", \"likert\"))  sjtab(data, ..., fun = c(\"xtab\", \"stackfrq\"))"},{"path":"https://strengejacke.github.io/sjPlot/reference/sjplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Wrapper to create plots and tables within a pipe-workflow — sjplot","text":"data data frame. May also grouped data frame (see 'Note' 'Examples'). ... Names variables plotted, also arguments passed sjPlot-functions. See 'Examples'. fun Plotting function. Refers function name sjPlot-functions. See 'Details' 'Examples'.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/sjplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Wrapper to create plots and tables within a pipe-workflow — sjplot","text":"See related sjp. sjt.-functions.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/sjplot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Wrapper to create plots and tables within a pipe-workflow — sjplot","text":"Following fun-values currently supported: \"aov1\" calls sjp.aov1. first             two variables data used (required) create plot. \"grpfrq\" calls plot_grpfrq. first             two variables data used (required) create plot. \"likert\" calls plot_likert. data             must data frame items plot. \"stackfrq\" calls tab_stackfrq.             data must data frame items create table. \"xtab\" calls plot_xtab tab_xtab.             first two variables data used (required)             create plot table.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/sjplot.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Wrapper to create plots and tables within a pipe-workflow — sjplot","text":"...-argument used, first, specify variables data       plotted, , second, name arguments       used subsequent plotting functions. Refer online-help       supported plotting-functions see valid arguments.       data may also grouped data frame (see group_by)       two grouping variables. Plots created subgroup .","code":""},{"path":[]},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_corr.html","id":null,"dir":"Reference","previous_headings":"","what":"Summary of correlations as HTML table — tab_corr","title":"Summary of correlations as HTML table — tab_corr","text":"Shows results computed correlation HTML table. Requires either                data.frame matrix correlation coefficients                returned cor-function.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_corr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summary of correlations as HTML table — tab_corr","text":"","code":"tab_corr(   data,   na.deletion = c(\"listwise\", \"pairwise\"),   corr.method = c(\"pearson\", \"spearman\", \"kendall\"),   title = NULL,   var.labels = NULL,   wrap.labels = 40,   show.p = TRUE,   p.numeric = FALSE,   fade.ns = TRUE,   val.rm = NULL,   digits = 3,   triangle = \"both\",   string.diag = NULL,   CSS = NULL,   encoding = NULL,   file = NULL,   use.viewer = TRUE,   remove.spaces = TRUE )"},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_corr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summary of correlations as HTML table — tab_corr","text":"data Matrix correlation coefficients returned cor-function, data.frame variables correlations columns computed. na.deletion Indicates missing values treated. May either \"listwise\" (default) \"pairwise\". May abbreviated. corr.method Indicates correlation computation method. May one \"pearson\" (default), \"spearman\" \"kendall\". May abbreviated. title String, used table caption. var.labels Character vector variable names, used label variables output. wrap.labels Numeric, determines many chars value, variable axis labels displayed one line line break inserted. show.p Logical, TRUE, p-values also printed. p.numeric Logical, TRUE, p-values printed numbers. FALSE (default), asterisks used. fade.ns Logical, TRUE (default), non-significant correlation-values appear faded (using lighter grey text color). See 'Note'. val.rm Specify number 0 1 suppress output correlation values smaller val.rm. absolute correlation values used, correlation value -.5 greater val.rm = .4 thus omitted. default, argument NULL, hence values shown table. correlation value specified value val.rm, still printed HTML table, made \"invisible\" white foreground color. can use CSS argument (\"css.valueremove\") change color appearance correlation value smaller limit specified val.rm. digits Amount decimals estimates triangle Indicates whether upper right (use \"upper\"), lower left (use \"lower\") (use \"\") triangles correlation table filled values. Default \"\". can specifiy inital letter . string.diag vector string values length ncol(data) (number correlated items) can used display content diagonal cells row column item identical (.e. \"self-correlation\"). defauilt, argument NULL diagnal cells empty. CSS list user-defined style-sheet-definitions, according official CSS syntax. See 'Details' package-vignette. encoding Character vector, indicating charset encoding used variable value labels. Default \"UTF-8\". Windows Systems, encoding = \"Windows-1252\" might necessary proper display special characters. file Destination file, output saved file. NULL (default), output saved temporary file opened either IDE's viewer pane default web browser. use.viewer Logical, TRUE, HTML table shown IDE's viewer pane. FALSE viewer available, HTML table opened web browser. remove.spaces Logical, TRUE, leading spaces removed lines final string contains html-data. Use , want remove parantheses html-tags. html-source may look less pretty, may help exporting html-tables office tools.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_corr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summary of correlations as HTML table — tab_corr","text":"Invisibly returns web page style sheet (page.style), web page content (page.content), complete html-output (page.complete) html-table inline-css use knitr (knitr) use.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_corr.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Summary of correlations as HTML table — tab_corr","text":"data matrix correlation coefficients returned       cor-function, p-values computed.       Thus, show.p, p.numeric fade.ns       effect data data.frame.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_corr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summary of correlations as HTML table — tab_corr","text":"","code":"if (FALSE) { # \\dontrun{ if (interactive()) {   # Data from the EUROFAMCARE sample dataset   library(sjmisc)   data(efc)    # retrieve variable and value labels   varlabs <- get_label(efc)    # recveive first item of COPE-index scale   start <- which(colnames(efc) == \"c83cop2\")   # recveive last item of COPE-index scale   end <- which(colnames(efc) == \"c88cop7\")    # create data frame with COPE-index scale   mydf <- data.frame(efc[, c(start:end)])   colnames(mydf) <- varlabs[c(start:end)]    # we have high correlations here, because all items   # belong to one factor.   tab_corr(mydf, p.numeric = TRUE)    # auto-detection of labels, only lower triangle   tab_corr(efc[, c(start:end)], triangle = \"lower\")    # auto-detection of labels, only lower triangle, all correlation   # values smaller than 0.3 are not shown in the table   tab_corr(efc[, c(start:end)], triangle = \"lower\", val.rm = 0.3)    # auto-detection of labels, only lower triangle, all correlation   # values smaller than 0.3 are printed in blue   tab_corr(efc[, c(start:end)], triangle = \"lower\",val.rm = 0.3,            CSS = list(css.valueremove = 'color:blue;')) }} # }"},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_df.html","id":null,"dir":"Reference","previous_headings":"","what":"Print data frames as HTML table. — tab_df","title":"Print data frames as HTML table. — tab_df","text":"functions print data frames HTML-table, showing   results RStudio's viewer pane web browser.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_df.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print data frames as HTML table. — tab_df","text":"","code":"tab_df(   x,   title = NULL,   footnote = NULL,   col.header = NULL,   show.type = FALSE,   show.rownames = FALSE,   show.footnote = FALSE,   alternate.rows = FALSE,   sort.column = NULL,   digits = 2,   encoding = \"UTF-8\",   CSS = NULL,   file = NULL,   use.viewer = TRUE,   ... )  tab_dfs(   x,   titles = NULL,   footnotes = NULL,   col.header = NULL,   show.type = FALSE,   show.rownames = FALSE,   show.footnote = FALSE,   alternate.rows = FALSE,   sort.column = NULL,   digits = 2,   encoding = \"UTF-8\",   CSS = NULL,   file = NULL,   use.viewer = TRUE,   rnames = NULL,   ... )"},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_df.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print data frames as HTML table. — tab_df","text":"x tab_df(), data frame; tab_dfs(), list data frames. title, titles, footnote, footnotes Character vector table caption(s) resp. footnote(s).  tab_df(), must character length 1; tab_dfs(), character vector length x (.e. one title footnote per data frame). col.header Character vector elements used column header table. NULL, column names x used column header. show.type Logical, TRUE, adds information variable type variable column. show.rownames Logical, TRUE, adds column data frame's rowname table output. show.footnote Logical, TRUE,adds summary footnote table. tab_df(), specify string footnote, tab_dfs() provide character vector footnotes. alternate.rows Logical, TRUE, rows printed alternatig colors (white light grey default). sort.column Numeric vector, indicating index column sorted. default, column sorted ascending order. Use negative index descending order, instance, sort.column = -3 sort third column descending order. Note first column rownames counted. digits Numeric, amount digits decimal point rounding values. encoding Character vector, indicating charset encoding used variable value labels. Default \"UTF-8\". Windows Systems, encoding = \"Windows-1252\" might necessary proper display special characters. CSS list user-defined style-sheet-definitions, according official CSS syntax. See 'Details' package-vignette. file Destination file, output saved file. NULL (default), output saved temporary file opened either IDE's viewer pane default web browser. use.viewer Logical, TRUE, HTML table shown IDE's viewer pane. FALSE viewer available, HTML table opened web browser. ... Currently used. rnames Character vector, can used set row names show.rownames=TRUE.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_df.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print data frames as HTML table. — tab_df","text":"list following items: web page style sheet (page.style), HTML content data frame (page.content), complete HTML page, including header, style sheet body (page.complete) HTML table inline-css use knitr (knitr) file path, HTML page saved disk (file)","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_df.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Print data frames as HTML table. — tab_df","text":"use CSS-argument?      CSS-argument, visual appearance tables   can modified. get overview style-sheet-classnames   used function, see return value page.style   details. Arguments list following syntax: class-name argument name style-definition must end semicolon can add style information default styles using +   (plus-sign) initial character argument attributes.   Examples: table = 'border:2px solid red;' solid 2-pixel table border red. summary = 'font-weight:bold;' bold fontweight summary row. lasttablerow = 'border-bottom: 1px dotted blue;' blue dotted border last table row. colnames = '+color:green' add green color formatting column names. arc = 'color:blue;' blue text color 2nd row. caption = '+color:red;' add red font-color default table caption style. See examples package-vignette.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_df.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Print data frames as HTML table. — tab_df","text":"HTML tables can either saved file manually opened   (use argument file) can saved temporary files   displayed RStudio Viewer pane (working RStudio)   opened default web browser. Displaying resp. opening   temporary file default behaviour.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_df.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Print data frames as HTML table. — tab_df","text":"","code":"if (FALSE) { # \\dontrun{ data(iris) data(mtcars) tab_df(iris[1:5, ]) tab_dfs(list(iris[1:5, ], mtcars[1:5, 1:5]))  # sort 2nd column ascending tab_df(iris[1:5, ], sort.column = 2)  # sort 2nd column descending tab_df(iris[1:5, ], sort.column = -2)} # }"},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_fa.html","id":null,"dir":"Reference","previous_headings":"","what":"Summary of factor analysis as HTML table — tab_fa","title":"Summary of factor analysis as HTML table — tab_fa","text":"Performs factor analysis data frame matrix                displays factors HTML                table, saves file.  case data frame used                parameter, Cronbach's Alpha value factor scale calculated,                .e. variables highest loading factor taken                reliability test. result alpha value factor dimension.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_fa.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summary of factor analysis as HTML table — tab_fa","text":"","code":"tab_fa(   data,   rotation = \"promax\",   method = c(\"ml\", \"minres\", \"wls\", \"gls\", \"pa\", \"minchi\", \"minrank\"),   nmbr.fctr = NULL,   fctr.load.tlrn = 0.1,   sort = FALSE,   title = \"Factor Analysis\",   var.labels = NULL,   wrap.labels = 40,   show.cronb = TRUE,   show.comm = FALSE,   alternate.rows = FALSE,   digits = 2,   CSS = NULL,   encoding = NULL,   file = NULL,   use.viewer = TRUE,   remove.spaces = TRUE )"},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_fa.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summary of factor analysis as HTML table — tab_fa","text":"data data frame used compute PCA, prcomp object. rotation Rotation factor loadings. May one \"varimax\", \"quartimax\", \"promax\", \"oblimin\", \"simplimax\", \"cluster\" \"none\". method factoring method used. \"ml\" maximum likelihood factor analysis (default). \"minres\" minimum residual (OLS), \"wls\" weighted least squares (WLS) solution, \"gls\" generalized weighted least squares (GLS), \"pa\" principal factor solution, \"minchi\" minimize sample size weighted chi square treating pairwise correlations different number subjects per pair. \"minrank\" minimum rank factor analysis. nmbr.fctr Number factors used calculating rotation. default, value NULL amount factors calculated according Kaiser-criteria. fctr.load.tlrn Specifies minimum difference variable needs factor loadings (components) order indicate clear loading just one factor diffusing factors. instance, variable 0.8, 0.82 0.84 factor loading 3 possible factors can clearly assigned just one factor thus removed principal component analysis. default, minimum difference loading values highest 2nd highest factor 0.1 sort logical, TRUE, sort loadings factors (items sorted terms greatest loading, descending order) title String, used table caption. var.labels Character vector variable names, used label variables output. wrap.labels Numeric, determines many chars value, variable axis labels displayed one line line break inserted. show.cronb Logical, TRUE (default), cronbach's alpha value factor scale calculated, .e. variables highest loading factor taken reliability test. result alpha value factor dimension. applies data data frame. show.comm Logical, TRUE, show communality column table. alternate.rows Logical, TRUE, rows printed alternatig colors (white light grey default). digits Amount decimals estimates CSS list user-defined style-sheet-definitions, according official CSS syntax. See 'Details' package-vignette. encoding Character vector, indicating charset encoding used variable value labels. Default \"UTF-8\". Windows Systems, encoding = \"Windows-1252\" might necessary proper display special characters. file Destination file, output saved file. NULL (default), output saved temporary file opened either IDE's viewer pane default web browser. use.viewer Logical, TRUE, HTML table shown IDE's viewer pane. FALSE viewer available, HTML table opened web browser. remove.spaces Logical, TRUE, leading spaces removed lines final string contains html-data. Use , want remove parantheses html-tags. html-source may look less pretty, may help exporting html-tables office tools.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_fa.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summary of factor analysis as HTML table — tab_fa","text":"Invisibly returns web page style sheet (page.style), web page content (page.content), complete html-output (page.complete), html-table inline-css use knitr (knitr), factor.index, .e. column index variable highest factor loading factor removed.items, .e. variables removed outside fctr.load.tlrn's range. use.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_fa.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Summary of factor analysis as HTML table — tab_fa","text":"method factor analysis relies functions       fa fa.parallel psych package.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_fa.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summary of factor analysis as HTML table — tab_fa","text":"","code":"if (FALSE) { # \\dontrun{ # Data from the EUROFAMCARE sample dataset library(sjmisc) library(GPArotation) data(efc)  # recveive first item of COPE-index scale start <- which(colnames(efc) == \"c82cop1\") # recveive last item of COPE-index scale end <- which(colnames(efc) == \"c90cop9\") # auto-detection of labels if (interactive()) {   tab_fa(efc[, start:end]) }} # }"},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_itemscale.html","id":null,"dir":"Reference","previous_headings":"","what":"Summary of item analysis of an item scale as HTML table — tab_itemscale","title":"Summary of item analysis of an item scale as HTML table — tab_itemscale","text":"function performs item analysis certain statistics                useful scale index development. resulting tables shown                viewer pane resp. webbrowser can saved file. Following statistics                computed item data frame: percentage missing values mean value standard deviation skew item difficulty item discrimination Cronbach's Alpha item removed scale mean (average) inter-item-correlation Optional, following statistics can computed well: kurstosis Shapiro-Wilk Normality Test factor.groups NULL, data frame df                splitted groups, assuming factor.groups indicate columns                data frame belong certain factor (see return value function tab_pca                example retrieving factor groups scale see examples details).","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_itemscale.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summary of item analysis of an item scale as HTML table — tab_itemscale","text":"","code":"tab_itemscale(   df,   factor.groups = NULL,   factor.groups.titles = \"auto\",   scale = FALSE,   min.valid.rowmean = 2,   alternate.rows = TRUE,   sort.column = NULL,   show.shapiro = FALSE,   show.kurtosis = FALSE,   show.corr.matrix = TRUE,   CSS = NULL,   encoding = NULL,   file = NULL,   use.viewer = TRUE,   remove.spaces = TRUE )  sjt.itemanalysis(   df,   factor.groups = NULL,   factor.groups.titles = \"auto\",   scale = FALSE,   min.valid.rowmean = 2,   alternate.rows = TRUE,   sort.column = NULL,   show.shapiro = FALSE,   show.kurtosis = FALSE,   show.corr.matrix = TRUE,   CSS = NULL,   encoding = NULL,   file = NULL,   use.viewer = TRUE,   remove.spaces = TRUE )"},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_itemscale.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summary of item analysis of an item scale as HTML table — tab_itemscale","text":"df data frame items. factor.groups NULL, df splitted sub-groups, item analysis carried groups. Must vector length ncol(df), item vector represents group number related columns df. factor.groups = \"auto\", principal component analysis Varimax rotation performed, resulting groups components used group index. See 'Examples'. factor.groups.titles Titles factor group used table caption component-table. Must character vector length length(unique(factor.groups)). Default \"auto\", means table standard caption Component x. Use NULL suppress table captions. scale Logical, TRUE, data frame's vectors scaled calculating Cronbach's Alpha value (see item_reliability). Recommended, variables different measures / scales. min.valid.rowmean Minimum amount valid values compute row means index scores. Default 2, .e. return values index.scores df.index.scores computed items least min.valid.rowmean per case (observation, technically, row). See mean_n details. alternate.rows Logical, TRUE, rows printed alternatig colors (white light grey default). sort.column Numeric vector, indicating index column sorted. default, column sorted ascending order. Use negative index descending order, instance, sort.column = -3 sort third column descending order. Note first column rownames counted. show.shapiro Logical, TRUE, Shapiro-Wilk normality test computed item. See shapiro.test details. show.kurtosis Logical, TRUE, kurtosis item also shown (see kurtosi describe psych-package details. show.corr.matrix Logical, TRUE (default), correlation matrix component's index score shown. applies factor.groups NULL df one group. First, case (df's row), sum variables (df's columns) scaled (using scale-function) represents \"total score\" component (component represented group factor.groups). , case (df's row) scales sum score component. Finally, correlation \"scale sum scores\" computed. CSS list user-defined style-sheet-definitions, according official CSS syntax. See 'Details' package-vignette. encoding Character vector, indicating charset encoding used variable value labels. Default \"UTF-8\". Windows Systems, encoding = \"Windows-1252\" might necessary proper display special characters. file Destination file, output saved file. NULL (default), output saved temporary file opened either IDE's viewer pane default web browser. use.viewer Logical, TRUE, HTML table shown IDE's viewer pane. FALSE viewer available, HTML table opened web browser. remove.spaces Logical, TRUE, leading spaces removed lines final string contains html-data. Use , want remove parantheses html-tags. html-source may look less pretty, may help exporting html-tables office tools.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_itemscale.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summary of item analysis of an item scale as HTML table — tab_itemscale","text":"Invisibly returns df.list: List data frames item analysis sub.group (complete, factor.groups NULL) index.scores: data frame standardized scale / index scores case (mean value scale items case) sub-group. ideal.item.diff: List vectors indicate ideal item difficulty item sub-group. Item difficulty differs items different levels. cronbach.values: List Cronbach's Alpha values overall item scale sub-group. knitr.list: List html-tables inline-css use knitr table (sub-group) knitr: html-table complete output inline-css use knitr complete.page: Complete html-output. factor.groups = NULL, list contains one elment, since just one          table printed complete scale indicated df. factor.groups          vector group-index-values, lists contain elements sub-group.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_itemscale.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Summary of item analysis of an item scale as HTML table — tab_itemscale","text":"Shapiro-Wilk Normality Test (see column W(p)) tests item distribution significantly different normal. Item difficulty range 0.2 0.8. Ideal value p+(1-p)/2 (mostly 0.5 0.8). item discrimination, acceptable values 0.20 higher; closer 1.00 better. See item_reliability details. case total Cronbach's Alpha value acceptable cut-0.7 (mostly index items), mean inter-item-correlation alternative measure indicate acceptability. Satisfactory range lies 0.2 0.4. See also item_intercor.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_itemscale.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Summary of item analysis of an item scale as HTML table — tab_itemscale","text":"Jorion N, Self B, James K, Schroeder L, DiBello L, Pellegrino J (2013) Classical Test Theory Analysis Dynamics Concept Inventory. (web) Briggs SR, Cheek JM (1986) role factor analysis development evaluation personality scales. Journal Personality, 54(1), 106-148. doi: 10.1111/j.1467-6494.1986.tb00391.x McLean S et al. (2013) Stigmatizing attitudes beliefs bulimia nervosa: Gender, age, education income variability community sample. International Journal Eating Disorders. doi: 10.1002/eat.22227 Trochim WMK (2008) Types Reliability.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_itemscale.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summary of item analysis of an item scale as HTML table — tab_itemscale","text":"","code":"# Data from the EUROFAMCARE sample dataset library(sjmisc) library(sjlabelled) data(efc)  # retrieve variable and value labels varlabs <- get_label(efc)  # recveive first item of COPE-index scale start <- which(colnames(efc) == \"c82cop1\") # recveive last item of COPE-index scale end <- which(colnames(efc) == \"c90cop9\")  # create data frame with COPE-index scale mydf <- data.frame(efc[, start:end]) colnames(mydf) <- varlabs[start:end]  if (FALSE) { # \\dontrun{ if (interactive()) {   tab_itemscale(mydf)    # auto-detection of labels   tab_itemscale(efc[, start:end])    # Compute PCA on Cope-Index, and perform a   # item analysis for each extracted factor.   indices <- tab_pca(mydf)$factor.index   tab_itemscale(mydf, factor.groups = indices)    # or, equivalent   tab_itemscale(mydf, factor.groups = \"auto\") }} # }"},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_model.html","id":null,"dir":"Reference","previous_headings":"","what":"Print regression models as HTML table — tab_model","title":"Print regression models as HTML table — tab_model","text":"tab_model() creates HTML tables regression models.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print regression models as HTML table — tab_model","text":"","code":"tab_model(   ...,   transform,   show.intercept = TRUE,   show.est = TRUE,   show.ci = 0.95,   show.ci50 = FALSE,   show.se = NULL,   show.std = NULL,   std.response = TRUE,   show.p = TRUE,   show.stat = FALSE,   show.df = FALSE,   show.zeroinf = TRUE,   show.r2 = TRUE,   show.icc = TRUE,   show.re.var = TRUE,   show.ngroups = TRUE,   show.fstat = FALSE,   show.aic = FALSE,   show.aicc = FALSE,   show.dev = FALSE,   show.loglik = FALSE,   show.obs = TRUE,   show.reflvl = FALSE,   terms = NULL,   rm.terms = NULL,   order.terms = NULL,   keep = NULL,   drop = NULL,   title = NULL,   pred.labels = NULL,   dv.labels = NULL,   wrap.labels = 25,   bootstrap = FALSE,   iterations = 1000,   seed = NULL,   robust = FALSE,   vcov.fun = NULL,   vcov.type = NULL,   vcov.args = NULL,   string.pred = \"Predictors\",   string.est = \"Estimate\",   string.std = \"std. Beta\",   string.ci = \"CI\",   string.se = \"std. Error\",   string.std_se = \"standardized std. Error\",   string.std_ci = \"standardized CI\",   string.p = \"p\",   string.std.p = \"std. p\",   string.df = \"df\",   string.stat = \"Statistic\",   string.std.stat = \"std. Statistic\",   string.resp = \"Response\",   string.intercept = \"(Intercept)\",   strings = NULL,   ci.hyphen = \"&nbsp;&ndash;&nbsp;\",   minus.sign = \"&#45;\",   collapse.ci = FALSE,   collapse.se = FALSE,   linebreak = TRUE,   col.order = c(\"est\", \"se\", \"std.est\", \"std.se\", \"ci\", \"std.ci\", \"ci.inner\", \"ci.outer\",     \"stat\", \"std.stat\", \"p\", \"std.p\", \"df.error\", \"response.level\"),   digits = 2,   digits.p = 3,   digits.rsq = 3,   digits.re = 2,   emph.p = TRUE,   p.val = NULL,   df.method = NULL,   p.style = c(\"numeric\", \"stars\", \"numeric_stars\", \"scientific\", \"scientific_stars\"),   p.threshold = c(0.05, 0.01, 0.001),   p.adjust = NULL,   case = \"parsed\",   auto.label = TRUE,   prefix.labels = c(\"none\", \"varname\", \"label\"),   bpe = \"median\",   CSS = css_theme(\"regression\"),   file = NULL,   use.viewer = TRUE,   encoding = \"UTF-8\" )"},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print regression models as HTML table — tab_model","text":"... One regression models, including glm's mixed models. May also list fitted models. See 'Examples'. transform character vector, naming function applied estimates confidence intervals. default, transform automatically use \"exp\" transformation applicable classes model (e.g. logistic poisson regression). Estimates linear models remain untransformed. Use NULL want raw, non-transformed estimates. show.intercept Logical, TRUE, intercepts printed. show.est Logical, TRUE, estimates printed. show.ci Either logical, TRUE, confidence intervals printed table; FALSE, confidence intervals omitted. numeric, 0 1, indicating range confidence intervals. show.ci50 Logical, TRUE, Bayesian models, second credible interval added table output. show.se Logical, TRUE, standard errors also printed. robust standard errors required, use arguments vcov.fun, vcov.type vcov.args (see standard_error details). show.std Indicates whether standardized beta-coefficients also printed, yes, type standardization done. See 'Details'. std.response Logical, whether response variable also standardized standardized coefficients requested. Setting std.response = TRUE show.std = TRUE behave complete data standardized fitting model. show.p Logical, TRUE, p-values also printed. show.stat Logical, TRUE, coefficients' test statistic also printed. show.df Logical, TRUE p.val = \"kr\", p-values linear mixed models based df Kenward-Rogers approximation. df-values printed. See p_value details. show.zeroinf Logical, TRUE model zero-inflated model part, also printed table. show.r2 Logical, TRUE, r-squared value also printed. Depending model, might pseudo-r-squared values, Bayesian r-squared etc. See r2 details. show.icc Logical, TRUE, prints intraclass correlation coefficient mixed models. See icc details. show.re.var Logical, TRUE, prints random effect variances mixed models. See get_variance details. show.ngroups Logical, TRUE, shows number random effects groups mixed models. show.fstat Logical, TRUE, F-statistics model printed table summary. option supported model types. show.aic Logical, TRUE, AIC value model printed table summary. show.aicc Logical, TRUE, second-order AIC value model printed table summary. show.dev Logical, TRUE, shows deviance model. show.loglik Logical, TRUE, shows log-Likelihood model. show.obs Logical, TRUE, number observations per model printed table summary. show.reflvl Logical, TRUE, additional row inserted table predictor type factor, indicate reference level related factor. terms Character vector names terms (variables) printed table. terms removed output. NULL, terms printed. Note term names must match names model's coefficients. factors, means variable name suffixed related factor level, category counts one term. E.g. rm.terms = \"t_name [2,3]\" remove terms \"t_name2\" \"t_name3\" (assuming variable t_name categorical least factor levels 2 3). Another example iris-dataset: terms = \"Species\" work, instead use terms = \"Species [versicolor,virginica]\". rm.terms Character vector names indicate terms removed output Counterpart terms. rm.terms = \"t_name\" remove term t_name. Default NULL, .e. terms used. factors, levels removed plot need explicitly indicated square brackets, match model's coefficient names, e.g. rm.terms = \"t_name [2,3]\" remove terms \"t_name2\" \"t_name3\" (assuming variable t_name categorical least factor levels 2 3). order.terms Numeric vector, indicating order coefficients plotted. See examples package-vignette. keep, drop Character containing regular expression pattern describes parameters included (keep) excluded (drop) returned data frame. keep may also named list regular expressions. non-matching parameters removed output. keep one element, merged operator regular expression pattern like : \"(one|two|three)\". See details ?parameters::model_parameters. title String, used table caption. pred.labels Character vector labels predictor variables. NULL, pred.labels used first table column predictors' names. default, auto.label = TRUE data labelled, term_labels called retrieve labels coefficients, used predictor labels. data labelled, format_parameters() used create pretty labels. pred.labels = \"\" auto.label = FALSE, raw variable names used model formula used predictor labels. pred.labels named vector, predictor labels (default, names model's coefficients) matched names pred.labels. ensures labels always match related predictor table, matter way predictors sorted. See 'Examples'. dv.labels Character vector labels dependent variables fitted models. dv.labels = \"\", row names dependent variables omitted table. wrap.labels Numeric, determines many chars value, variable axis labels displayed one line line break inserted. bootstrap Logical, TRUE, returns bootstrapped estimates.. iterations Numeric, number bootstrap iterations (default 1000). seed Numeric, number seed replicate bootstrapped estimates. NULL, uses random seed. robust Deprecated. Please use vcov.fun directly specify estimation variance-covariance matrix. vcov.fun Variance-covariance matrix used compute uncertainty estimates (e.g., robust standard errors). argument accepts covariance matrix, function returns covariance matrix, string identifies function used compute covariance matrix. See model_parameters(). vcov.type Deprecated. type-argument now included vcov.args. vcov.args List arguments passed function identified vcov.fun argument. function typically supplied sandwich clubSandwich packages. Please refer documentation (e.g., ?sandwich::vcovHAC) see list available arguments. string.pred Character vector,used headline predictor column. Default \"Predictors\". string.est Character vector, used column heading coefficients. Default based response scale, e.g. logistic regression models, \"Odds Ratios\" chosen, Poisson models \"Incidence Rate Ratios\" etc. Default specified \"Estimate\". string.std Character vector, used column heading standardized beta coefficients. Default \"std. Beta\". string.ci Character vector, used column heading confidence interval values. Default \"CI\". string.se Character vector, used column heading standard error values. Default \"std. Error\". string.std_se Character vector, used column heading standard error standardized coefficients. Default \"standardized std. Error\". string.std_ci Character vector, used column heading confidence intervals standardized coefficients. Default \"standardized std. Error\". string.p Character vector, used column heading p values. Default \"p\". string.std.p Character vector, used column heading p values. Default \"std. p\". string.df Character vector, used column heading degrees freedom. Default \"df\". string.stat Character vector, used test statistic. Default \"Statistic\". string.std.stat Character vector, used test statistic. Default \"std. Statistic\". string.resp Character vector, used column heading response level multinominal categorical models. Default \"Response\". string.intercept Character vector, used name intercept parameter. Default \"(Intercept)\". strings Named character vector, alternative arguments like string.ci string.p etc. name (lhs) must one string-indicator aforementioned arguments, value (rhs) string used column heading. E.g., strings = c(ci = \"Conf.Int.\", se = \"std. Err\") equivalent setting string.ci = \"Conf.Int.\", string.se = \"std. Err\". ci.hyphen Character vector, indicating hyphen confidence interval range. May HTML entity. See 'Examples'. minus.sign string, indicating minus sign negative numbers. May HTML entity. See 'Examples'. collapse.ci Logical, FALSE, CI values shown separate table column. collapse.se Logical, FALSE, SE values shown separate table column. linebreak Logical, TRUE collapse.ci = FALSE collapse.se = FALSE, inserts line break estimate CI resp. SE values. FALSE, values printed line estimate values. col.order Character vector, indicating columns printed order. Column names excluded col.order shown table output. However, column names included, shown table related argument (like show.est \"estimate\") set TRUE another valid value. Table columns printed order appear col.order. digits Amount decimals estimates digits.p Amount decimals p-values digits.rsq Amount decimals r-squared values digits.re Amount decimals random effects part summary table. emph.p Logical, TRUE, significant p-values shown bold faced. df.method, p.val Method computing degrees freedom p-values, standard errors confidence intervals (CI). applies mixed models. Use df.method = \"wald\" faster, less precise computation. use residual degrees freedom (returned df.residual()) linear mixed models, Inf degrees freedom model families. df.method = \"kenward\" (df.method = \"kr\") uses Kenward-Roger approximation degrees freedom. df.method = \"satterthwaite\" uses Satterthwaite's approximation \"ml1\" uses \"m-l-1\" heuristic see degrees_of_freedom details). Use show.df = TRUE show approximated degrees freedom coefficient. p.style Character, indicating p-values printed numeric value (\"numeric\"), 'stars' (asterisks) (\"stars\"), scientific (\"scientific\"). Scientific numeric style can combined \"stars\", e.g. \"numeric_stars\" p.threshold Numeric vector length 3, indicating treshold annotating p-values asterisks. applies p.style = \"asterisk\". p.adjust Character vector, NULL, indicates method adjust p-values. See p.adjust details. case Desired target case. Labels automatically converted specified character case. See snakecase::to_any_case() details argument. default, case specified, set \"parsed\", unless prefix.labels \"none\". prefix.labels either \"label\" (\"l\") \"varname\" (\"v\") case specified, set NULL - convenient default prefixing labels. auto.label Logical, TRUE (default),  data labelled,  term_labels called retrieve labels  coefficients, used predictor labels. data  labelled, format_parameters()  used create pretty labels. auto.label = FALSE, original variable names value labels (factor levels) used. prefix.labels Indicates whether value labels categorical variables prefixed, e.g. variable name variable label. See argument prefix term_labels details. bpe Stan-models (fitted rstanarm- brms-package), Bayesian point estimate , default, median posterior distribution. Use bpe define functions calculate Bayesian point estimate. bpe needs character naming specific function, passed fun-argument typical_value. , bpe = \"mean\" calculate mean value posterior distribution. CSS list user-defined style-sheet-definitions, according official CSS syntax. See 'Details' package-vignette. file Destination file, output saved file. NULL (default), output saved temporary file opened either IDE's viewer pane default web browser. use.viewer Logical, TRUE, HTML table shown IDE's viewer pane. FALSE viewer available, HTML table opened web browser. encoding Character vector, indicating charset encoding used variable value labels. Default \"UTF-8\". Windows Systems, encoding = \"Windows-1252\" might necessary proper display special characters.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print regression models as HTML table — tab_model","text":"Invisibly returns web page style sheet (page.style), web page content (page.content), complete html-output (page.complete) html-table inline-css use knitr (knitr) use.","code":""},{"path":[]},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_model.html","id":"standardized-estimates","dir":"Reference","previous_headings":"","what":"Standardized Estimates","title":"Print regression models as HTML table — tab_model","text":"Default standardization done completely refitting model   standardized data. Hence, approach equal standardizing   variables fitting model, particularly recommended   complex models include interactions transformations (e.g., polynomial   spline terms). show.std = \"std2\", standardization estimates   follows Gelman's (2008)   suggestion, rescaling estimates dividing two standard deviations   instead just one. Resulting coefficients directly comparable   untransformed binary predictors. backward compatibility reasons,   show.std also may logical value; TRUE, normal standardized   estimates printed (effect show.std = \"std\"). Use   show.std = NULL (default) show.std = FALSE, standardization   required.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_model.html","id":"how-do-i-use-css-argument-","dir":"Reference","previous_headings":"","what":"How do I use CSS-argument?","title":"Print regression models as HTML table — tab_model","text":"CSS-argument, visual appearance tables    can modified. get overview style-sheet-classnames    used function, see return value page.style details.    Arguments list following syntax: class-names \"css.\"-prefix argument name style-definition must end semicolon can add style information default styles using + (plus-sign)    initial character argument attributes. Examples: css.table = 'border:2px solid red;' solid 2-pixel table border red. css.summary = 'font-weight:bold;' bold fontweight summary row. css.lasttablerow = 'border-bottom: 1px dotted blue;' blue dotted border last table row. css.colnames = '+color:green' add green color formatting column names. css.arc = 'color:blue;' blue text color 2nd row. css.caption = '+color:red;' add red font-color default table caption style.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_model.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Print regression models as HTML table — tab_model","text":"HTML tables can either saved file manually opened (use argument file)         can saved temporary files displayed RStudio Viewer pane (working RStudio)         opened default web browser. Displaying resp. opening temporary file         default behaviour (.e. file = NULL).                  Examples shown three vignettes:         Summary Regression Models HTML Table,         Summary Mixed Models HTML Table         Summary Bayesian Models HTML Table.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_pca.html","id":null,"dir":"Reference","previous_headings":"","what":"Summary of principal component analysis as HTML table — tab_pca","title":"Summary of principal component analysis as HTML table — tab_pca","text":"Performes principle component analysis data frame matrix                (varimax oblimin rotation) displays factor solution HTML                table, saves file.  case data frame used                parameter, Cronbach's Alpha value factor scale calculated,                .e. variables highest loading factor taken                reliability test. result alpha value factor dimension.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_pca.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summary of principal component analysis as HTML table — tab_pca","text":"","code":"tab_pca(   data,   rotation = c(\"varimax\", \"quartimax\", \"promax\", \"oblimin\", \"simplimax\", \"cluster\",     \"none\"),   nmbr.fctr = NULL,   fctr.load.tlrn = 0.1,   title = \"Principal Component Analysis\",   var.labels = NULL,   wrap.labels = 40,   show.cronb = TRUE,   show.msa = FALSE,   show.var = FALSE,   alternate.rows = FALSE,   digits = 2,   string.pov = \"Proportion of Variance\",   string.cpov = \"Cumulative Proportion\",   CSS = NULL,   encoding = NULL,   file = NULL,   use.viewer = TRUE,   remove.spaces = TRUE )"},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_pca.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summary of principal component analysis as HTML table — tab_pca","text":"data data frame used compute PCA, prcomp object. rotation Rotation factor loadings. May one \"varimax\", \"quartimax\", \"promax\", \"oblimin\", \"simplimax\", \"cluster\" \"none\". nmbr.fctr Number factors used calculating rotation. default, value NULL amount factors calculated according Kaiser-criteria. fctr.load.tlrn Specifies minimum difference variable needs factor loadings (components) order indicate clear loading just one factor diffusing factors. instance, variable 0.8, 0.82 0.84 factor loading 3 possible factors can clearly assigned just one factor thus removed principal component analysis. default, minimum difference loading values highest 2nd highest factor 0.1 title String, used table caption. var.labels Character vector variable names, used label variables output. wrap.labels Numeric, determines many chars value, variable axis labels displayed one line line break inserted. show.cronb Logical, TRUE (default), cronbach's alpha value factor scale calculated, .e. variables highest loading factor taken reliability test. result alpha value factor dimension. applies data data frame. show.msa Logical, TRUE, shows additional column measure sampling adequacy according dor component. show.var Logical, TRUE, proportions variances component well cumulative variance shown table footer. alternate.rows Logical, TRUE, rows printed alternatig colors (white light grey default). digits Amount decimals estimates string.pov String table row contains proportions variances. default, \"Proportion Variance\" used. string.cpov String table row contains cumulative variances. default, \"Cumulative Proportion\" used. CSS list user-defined style-sheet-definitions, according official CSS syntax. See 'Details' package-vignette. encoding Character vector, indicating charset encoding used variable value labels. Default \"UTF-8\". Windows Systems, encoding = \"Windows-1252\" might necessary proper display special characters. file Destination file, output saved file. NULL (default), output saved temporary file opened either IDE's viewer pane default web browser. use.viewer Logical, TRUE, HTML table shown IDE's viewer pane. FALSE viewer available, HTML table opened web browser. remove.spaces Logical, TRUE, leading spaces removed lines final string contains html-data. Use , want remove parantheses html-tags. html-source may look less pretty, may help exporting html-tables office tools.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_pca.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summary of principal component analysis as HTML table — tab_pca","text":"Invisibly returns web page style sheet (page.style), web page content (page.content), complete html-output (page.complete), html-table inline-css use knitr (knitr), factor.index, .e. column index variable highest factor loading factor removed.items, .e. variables removed outside fctr.load.tlrn's range. use.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_pca.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summary of principal component analysis as HTML table — tab_pca","text":"","code":"if (FALSE) { # \\dontrun{ # Data from the EUROFAMCARE sample dataset library(sjmisc) data(efc)  # recveive first item of COPE-index scale start <- which(colnames(efc) == \"c82cop1\") # recveive last item of COPE-index scale end <- which(colnames(efc) == \"c90cop9\") # auto-detection of labels if (interactive()) {   tab_pca(efc[, start:end]) }} # }"},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_stackfrq.html","id":null,"dir":"Reference","previous_headings":"","what":"Summary of stacked frequencies as HTML table — tab_stackfrq","title":"Summary of stacked frequencies as HTML table — tab_stackfrq","text":"Shows results stacked frequencies (likert scales) HTML table.                function useful several items identical scale/categories                printed table compare distributions (e.g.                plotting scales like SF, Barthel-Index, Quality--Life-scales etc.).","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_stackfrq.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summary of stacked frequencies as HTML table — tab_stackfrq","text":"","code":"tab_stackfrq(   items,   weight.by = NULL,   title = NULL,   var.labels = NULL,   value.labels = NULL,   wrap.labels = 20,   sort.frq = NULL,   alternate.rows = FALSE,   digits = 2,   string.total = \"N\",   string.na = \"NA\",   show.n = FALSE,   show.total = FALSE,   show.na = FALSE,   show.skew = FALSE,   show.kurtosis = FALSE,   digits.stats = 2,   file = NULL,   encoding = NULL,   CSS = NULL,   use.viewer = TRUE,   remove.spaces = TRUE )"},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_stackfrq.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summary of stacked frequencies as HTML table — tab_stackfrq","text":"items Data frame, grouped data frame, column representing one item. weight.Vector weights applied weight cases. Must vector length input vector. Default NULL, weights used. title String, used table caption. var.labels Character vector variable names, used label variables output. value.labels Character vector (list character vectors) value labels supplied variables, used label variable values output. wrap.labels Numeric, determines many chars value, variable axis labels displayed one line line break inserted. sort.frq logical, indicates whether items ordered highest count first last category items. Use \"first.asc\" order ascending lowest count first category, \"first.desc\" order descending lowest count first category, \"last.asc\" order ascending lowest count last category, \"last.desc\" order descending lowest count last category, NULL (default) sorting. alternate.rows Logical, TRUE, rows printed alternatig colors (white light grey default). digits Numeric, amount digits decimal point rounding values. string.total label total N column. string.na label missing column/row. show.n logical, TRUE, adds total number cases group category labels. show.total logical, TRUE, additional column item's total N printed. show.na logical, TRUE, NA's (missing values) added output. show.skew logical, TRUE, additional column item's skewness printed. skewness retrieved describe-function psych-package. show.kurtosis Logical, TRUE, kurtosis item also shown (see kurtosi describe psych-package details. digits.stats amount digits rounding skewness kurtosis valuess. Default 2, .e. skewness kurtosis values 2 digits decimal point. file Destination file, output saved file. NULL (default), output saved temporary file opened either IDE's viewer pane default web browser. encoding Character vector, indicating charset encoding used variable value labels. Default \"UTF-8\". Windows Systems, encoding = \"Windows-1252\" might necessary proper display special characters. CSS list user-defined style-sheet-definitions, according official CSS syntax. See 'Details' package-vignette. use.viewer Logical, TRUE, HTML table shown IDE's viewer pane. FALSE viewer available, HTML table opened web browser. remove.spaces Logical, TRUE, leading spaces removed lines final string contains html-data. Use , want remove parantheses html-tags. html-source may look less pretty, may help exporting html-tables office tools.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_stackfrq.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summary of stacked frequencies as HTML table — tab_stackfrq","text":"Invisibly returns web page style sheet (page.style), web page content (page.content), complete html-output (page.complete) html-table inline-css use knitr (knitr) use.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_stackfrq.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summary of stacked frequencies as HTML table — tab_stackfrq","text":"","code":"# ------------------------------- # random sample # ------------------------------- # prepare data for 4-category likert scale, 5 items likert_4 <- data.frame(   as.factor(sample(1:4, 500, replace = TRUE, prob = c(0.2, 0.3, 0.1, 0.4))),   as.factor(sample(1:4, 500, replace = TRUE, prob = c(0.5, 0.25, 0.15, 0.1))),   as.factor(sample(1:4, 500, replace = TRUE, prob = c(0.25, 0.1, 0.4, 0.25))),   as.factor(sample(1:4, 500, replace = TRUE, prob = c(0.1, 0.4, 0.4, 0.1))),   as.factor(sample(1:4, 500, replace = TRUE, prob = c(0.35, 0.25, 0.15, 0.25))) )  # create labels levels_4 <- c(\"Independent\", \"Slightly dependent\",               \"Dependent\", \"Severely dependent\")  # create item labels items <- c(\"Q1\", \"Q2\", \"Q3\", \"Q4\", \"Q5\")  # plot stacked frequencies of 5 (ordered) item-scales if (FALSE) { # \\dontrun{ if (interactive()) {   tab_stackfrq(likert_4, value.labels = levels_4, var.labels = items)    # -------------------------------   # Data from the EUROFAMCARE sample dataset   #  Auto-detection of labels   # -------------------------------   data(efc)   # recveive first item of COPE-index scale   start <- which(colnames(efc) == \"c82cop1\")   # recveive first item of COPE-index scale   end <- which(colnames(efc) == \"c90cop9\")    tab_stackfrq(efc[, c(start:end)], alternate.rows = TRUE)    tab_stackfrq(efc[, c(start:end)], alternate.rows = TRUE,                show.n = TRUE, show.na = TRUE)    # --------------------------------   # User defined style sheet   # --------------------------------   tab_stackfrq(efc[, c(start:end)], alternate.rows = TRUE,                show.total = TRUE, show.skew = TRUE, show.kurtosis = TRUE,                CSS = list(css.ncol = \"border-left:1px dotted black;\",                           css.summary = \"font-style:italic;\")) } } # }"},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_xtab.html","id":null,"dir":"Reference","previous_headings":"","what":"Summary of contingency tables as HTML table — tab_xtab","title":"Summary of contingency tables as HTML table — tab_xtab","text":"Shows contingency tables HTML file browser viewer pane, saves file.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_xtab.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summary of contingency tables as HTML table — tab_xtab","text":"","code":"tab_xtab(   var.row,   var.col,   weight.by = NULL,   title = NULL,   var.labels = NULL,   value.labels = NULL,   wrap.labels = 20,   show.obs = TRUE,   show.cell.prc = FALSE,   show.row.prc = FALSE,   show.col.prc = FALSE,   show.exp = FALSE,   show.legend = FALSE,   show.na = FALSE,   show.summary = TRUE,   drop.empty = TRUE,   statistics = c(\"auto\", \"cramer\", \"phi\", \"spearman\", \"kendall\", \"pearson\", \"fisher\"),   string.total = \"Total\",   digits = 1,   tdcol.n = \"black\",   tdcol.expected = \"#339999\",   tdcol.cell = \"#993333\",   tdcol.row = \"#333399\",   tdcol.col = \"#339933\",   emph.total = FALSE,   emph.color = \"#f8f8f8\",   prc.sign = \"&nbsp;&#37;\",   hundret = \"100.0\",   CSS = NULL,   encoding = NULL,   file = NULL,   use.viewer = TRUE,   remove.spaces = TRUE,   ... )  sjt.xtab(   var.row,   var.col,   weight.by = NULL,   title = NULL,   var.labels = NULL,   value.labels = NULL,   wrap.labels = 20,   show.obs = TRUE,   show.cell.prc = FALSE,   show.row.prc = FALSE,   show.col.prc = FALSE,   show.exp = FALSE,   show.legend = FALSE,   show.na = FALSE,   show.summary = TRUE,   drop.empty = TRUE,   statistics = c(\"auto\", \"cramer\", \"phi\", \"spearman\", \"kendall\", \"pearson\", \"fisher\"),   string.total = \"Total\",   digits = 1,   tdcol.n = \"black\",   tdcol.expected = \"#339999\",   tdcol.cell = \"#993333\",   tdcol.row = \"#333399\",   tdcol.col = \"#339933\",   emph.total = FALSE,   emph.color = \"#f8f8f8\",   prc.sign = \"&nbsp;&#37;\",   hundret = \"100.0\",   CSS = NULL,   encoding = NULL,   file = NULL,   use.viewer = TRUE,   remove.spaces = TRUE,   ... )"},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_xtab.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summary of contingency tables as HTML table — tab_xtab","text":"var.row Variable displayed table rows. var.col Cariable displayed table columns. weight.Vector weights applied weight cases. Must vector length input vector. Default NULL, weights used. title String, used table caption. var.labels Character vector variable names, used label variables output. value.labels Character vector (list character vectors) value labels supplied variables, used label variable values output. wrap.labels Numeric, determines many chars value, variable axis labels displayed one line line break inserted. show.obs Logical, TRUE, observed values shown show.cell.prc Logical, TRUE, cell percentage values shown show.row.prc Logical, TRUE, row percentage values shown show.col.prc Logical, TRUE, column percentage values shown show.exp Logical, TRUE, expected values also shown show.legend logical, TRUE, depending plot type function, legend added plot. show.na logical, TRUE, NA's (missing values) added output. show.summary Logical, TRUE, summary row chi-squared statistics, degrees freedom Cramer's V Phi coefficient p-value chi-squared statistics. drop.empty Logical, TRUE variable's values labeled, values / factor levels occurrence data omitted output. FALSE, labeled values observations still printed table (frequency 0). statistics Name measure association computed. May one \"auto\", \"cramer\", \"phi\", \"spearman\", \"kendall\", \"pearson\" \"fisher\". See xtab_statistics. string.total Character label total column / row header digits Amount decimals estimates tdcol.n Color highlighting count (observed) values table cells. Default black. tdcol.expected Color highlighting expected values table cells. Default cyan. tdcol.cell Color highlighting cell percentage values table cells. Default red. tdcol.row Color highlighting row percentage values table cells. Default blue. tdcol.col Color highlighting column percentage values table cells. Default green. emph.total Logical, TRUE, total column row emphasized different background color. See emph.color. emph.color Logical, emph.total = TRUE, color value used painting background total column row. Default light grey. prc.sign percentage sign printed table cells, HTML-format. Default \"&nbsp;%\", hence percentage sign non-breaking-space percentage value. hundret Default value indicates 100-percent column-sums (since rounding values may lead non-exact results). Default \"100.0\". CSS list user-defined style-sheet-definitions, according official CSS syntax. See 'Details' package-vignette. encoding String, indicating charset encoding used variable value labels. Default NULL, encoding auto-detected depending platform (e.g., \"UTF-8\" Unix \"Windows-1252\" Windows OS). Change encoding specific chars properly displayed (e.g. German umlauts). file Destination file, output saved file. NULL (default), output saved temporary file opened either IDE's viewer pane default web browser. use.viewer Logical, TRUE, HTML table shown IDE's viewer pane. FALSE viewer available, HTML table opened web browser. remove.spaces Logical, TRUE, leading spaces removed lines final string contains html-data. Use , want remove parantheses html-tags. html-source may look less pretty, may help exporting html-tables office tools. ... arguments, currently passed test statistics functions chisq.test() fisher.test().","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_xtab.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summary of contingency tables as HTML table — tab_xtab","text":"Invisibly returns web page style sheet (page.style), web page content (page.content), complete html-output (page.complete) html-table inline-css use knitr (knitr) use.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_xtab.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summary of contingency tables as HTML table — tab_xtab","text":"","code":"# prepare sample data set data(efc)  # print simple cross table with labels if (FALSE) { # \\dontrun{ if (interactive()) {   tab_xtab(efc$e16sex, efc$e42dep)    # print cross table with manually set   # labels and expected values   tab_xtab(     efc$e16sex,     efc$e42dep,     var.labels = c(\"Elder's gender\", \"Elder's dependency\"),     show.exp = TRUE   )    # print minimal cross table with labels, total col/row highlighted   tab_xtab(efc$e16sex, efc$e42dep, show.cell.prc = FALSE, emph.total = TRUE)    # User defined style sheet   tab_xtab(efc$e16sex, efc$e42dep,            CSS = list(css.table = \"border: 2px solid;\",                       css.tdata = \"border: 1px solid;\",                       css.horline = \"border-bottom: double blue;\"))    # ordinal data, use Kendall's tau   tab_xtab(efc$e42dep, efc$quol_5, statistics = \"kendall\")    # calculate Spearman's rho, with continuity correction   tab_xtab(     efc$e42dep,     efc$quol_5,     statistics = \"spearman\",     exact = FALSE,     continuity = TRUE   ) } } # }"},{"path":"https://strengejacke.github.io/sjPlot/reference/view_df.html","id":null,"dir":"Reference","previous_headings":"","what":"View structure of labelled data frames — view_df","title":"View structure of labelled data frames — view_df","text":"Save (show) content imported SPSS, SAS Stata data file,                similar labelled data.frame, HTML table.                quick overview shows variable ID number, name, label,                type associated value labels. result can                considered \"codeplan\" data frame.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/view_df.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"View structure of labelled data frames — view_df","text":"","code":"view_df(   x,   weight.by = NULL,   alternate.rows = TRUE,   show.id = TRUE,   show.type = FALSE,   show.values = TRUE,   show.string.values = FALSE,   show.labels = TRUE,   show.frq = FALSE,   show.prc = FALSE,   show.wtd.frq = FALSE,   show.wtd.prc = FALSE,   show.na = FALSE,   max.len = 15,   sort.by.name = FALSE,   wrap.labels = 50,   verbose = FALSE,   CSS = NULL,   encoding = NULL,   file = NULL,   use.viewer = TRUE,   remove.spaces = TRUE )"},{"path":"https://strengejacke.github.io/sjPlot/reference/view_df.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"View structure of labelled data frames — view_df","text":"x (labelled) data frame, imported read_spss, read_sas read_stata function, similar labelled data frame (see set_label set_labels). weight.Name variable x indicated vector weights applied weight  observations. Default NULL, weights used. alternate.rows Logical, TRUE, rows printed alternatig colors (white light grey default). show.id Logical, TRUE (default), variable ID shown first column. show.type Logical, TRUE, adds information variable type variable column. show.values Logical, TRUE (default), variable values shown additional column. show.string.values Logical, TRUE, elements character vectors also shown. default, omitted due possibly overlengthy tables. show.labels Logical, TRUE (default), value labels shown additional column. show.frq Logical, TRUE, additional column frequencies variable shown. show.prc Logical, TRUE, additional column percentage frequencies variable shown. show.wtd.frq Logical, TRUE, additional column weighted frequencies variable shown. Weights strem weight.. show.wtd.prc Logical, TRUE, additional column weighted percentage frequencies variable shown. Weights strem weight.. show.na logical, TRUE, NA's (missing values) added output. max.len Numeric, indicates many values value labels per variable shown. Useful variables many different values, output can truncated. sort..name Logical, TRUE, rows sorted according variable names. default, rows (variables) ordered according order data frame. wrap.labels Numeric, determines many chars value, variable axis labels displayed one line line break inserted. verbose Logical, TRUE, progress bar displayed creating output. CSS list user-defined style-sheet-definitions, according official CSS syntax. See 'Details' package-vignette. encoding Character vector, indicating charset encoding used variable value labels. Default \"UTF-8\". Windows Systems, encoding = \"Windows-1252\" might necessary proper display special characters. file Destination file, output saved file. NULL (default), output saved temporary file opened either IDE's viewer pane default web browser. use.viewer Logical, TRUE, HTML table shown IDE's viewer pane. FALSE viewer available, HTML table opened web browser. remove.spaces Logical, TRUE, leading spaces removed lines final string contains html-data. Use , want remove parantheses html-tags. html-source may look less pretty, may help exporting html-tables office tools.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/view_df.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"View structure of labelled data frames — view_df","text":"Invisibly returns web page style sheet (page.style), web page content (page.content), complete html-output (page.complete) html-table inline-css use knitr (knitr) use.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/view_df.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"View structure of labelled data frames — view_df","text":"","code":"if (FALSE) { # \\dontrun{ # init dataset data(efc)  # view variables view_df(efc)  # view variables w/o values and value labels view_df(efc, show.values = FALSE, show.labels = FALSE)  # view variables including variable typed, orderd by name view_df(efc, sort.by.name = TRUE, show.type = TRUE)  # User defined style sheet view_df(efc,         CSS = list(css.table = \"border: 2px solid;\",                    css.tdata = \"border: 1px solid;\",                    css.arc = \"color:blue;\"))} # }"},{"path":[]},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"bug-fixes-2-8-17","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"sjPlot 2.8.17","text":"Fixed issues renamed arguments upstream package ggeffects.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"sjplot-2815","dir":"Changelog","previous_headings":"","what":"sjPlot 2.8.15","title":"sjPlot 2.8.15","text":"CRAN release: 2023-08-17 Minor fix vignettes.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"sjplot-2814","dir":"Changelog","previous_headings":"","what":"sjPlot 2.8.14","title":"sjPlot 2.8.14","text":"CRAN release: 2023-04-02 tab_model(), plot_model() plot_models() get std.response argument, include exclude response variable standardization.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"bug-fixes-2-8-14","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"sjPlot 2.8.14","text":"Fixed test issues internet connection avaiable. Minor fixes improvements.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"sjplot-2813","dir":"Changelog","previous_headings":"","what":"sjPlot 2.8.13","title":"sjPlot 2.8.13","text":"CRAN release: 2023-03-13","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"bug-fixes-2-8-13","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"sjPlot 2.8.13","text":"Fixed issue knit_print() generics.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"sjplot-2812","dir":"Changelog","previous_headings":"","what":"sjPlot 2.8.12","title":"sjPlot 2.8.12","text":"CRAN release: 2022-11-19","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"general-2-8-12","dir":"Changelog","previous_headings":"","what":"General","title":"sjPlot 2.8.12","text":"Minor changes work properly updates upstream packages various smaller bug fixes improvements.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"sjplot-2811","dir":"Changelog","previous_headings":"","what":"sjPlot 2.8.11","title":"sjPlot 2.8.11","text":"CRAN release: 2022-08-07","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"general-2-8-11","dir":"Changelog","previous_headings":"","what":"General","title":"sjPlot 2.8.11","text":"Minor changes work properly updates upstream packages various smaller bug fixes improvements.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"sjplot-2810","dir":"Changelog","previous_headings":"","what":"sjPlot 2.8.10","title":"sjPlot 2.8.10","text":"CRAN release: 2021-11-26","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"general-2-8-10","dir":"Changelog","previous_headings":"","what":"General","title":"sjPlot 2.8.10","text":"Minor changes work properly updates downstream packages various smaller bug fixes improvements.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"sjplot-289","dir":"Changelog","previous_headings":"","what":"sjPlot 2.8.9","title":"sjPlot 2.8.9","text":"CRAN release: 2021-07-10","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"bug-fixes-2-8-9","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"sjPlot 2.8.9","text":"Fixed issues CRAN checks.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"sjplot-288","dir":"Changelog","previous_headings":"","what":"sjPlot 2.8.8","title":"sjPlot 2.8.8","text":"CRAN release: 2021-05-25","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"changes-to-functions-2-8-8","dir":"Changelog","previous_headings":"","what":"Changes to functions","title":"sjPlot 2.8.8","text":"tab_model() now works properly forthcoming parameters update.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"bug-fixes-2-8-8","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"sjPlot 2.8.8","text":"plot_models() work properly Bayesian models.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"sjplot-287","dir":"Changelog","previous_headings":"","what":"sjPlot 2.8.7","title":"sjPlot 2.8.7","text":"CRAN release: 2021-01-10","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"changes-to-functions-2-8-7","dir":"Changelog","previous_headings":"","what":"Changes to functions","title":"sjPlot 2.8.7","text":"tab_model() also gains encoding argument. tab_df() tab_dfs() longer set argument show.rownames TRUE. Therefore, functions now use row numbers row names, rownames present. tab_dfs() also gains digits argument.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"bug-fixes-2-8-7","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"sjPlot 2.8.7","text":"Argument df.method tab_model() accept available options documented. Fix CRAN check issues (warnings new R-devel).","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"sjplot-286","dir":"Changelog","previous_headings":"","what":"sjPlot 2.8.6","title":"sjPlot 2.8.6","text":"CRAN release: 2020-10-28","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"changes-to-functions-2-8-6","dir":"Changelog","previous_headings":"","what":"Changes to functions","title":"sjPlot 2.8.6","text":"dv.labels = \"\" tab_model(), row names dependent variables omitted.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"bug-fixes-2-8-6","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"sjPlot 2.8.6","text":"Fix CRAN check issues (removed cross-references archived packages). minus.sign argument tab_model() now works. show.std = TRUE tab_model() exponentiate standardized coefficients non-Gaussian models.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"sjplot-285","dir":"Changelog","previous_headings":"","what":"sjPlot 2.8.5","title":"sjPlot 2.8.5","text":"CRAN release: 2020-09-24","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"changes-to-functions-2-8-5","dir":"Changelog","previous_headings":"","what":"Changes to functions","title":"sjPlot 2.8.5","text":"tab_model() gains argument df.method, replace less generic p.val argument future. Currently, df.method alias p.val.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"bug-fixes-2-8-5","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"sjPlot 2.8.5","text":"Fixed issue wrong n’s plot_stackfrq() weights applied. Fixed issue plot_stackfrq() weights applied items sorted. Fixed issue plot_models() models without intercept. Fixed issue wrong legend labelling plot_models() showing p-stars. Fixed issue plot_model() type = \"int\" detecting interaction terms partly parenthesis (like * (b + c)). Fixed issue tab_model() arguments show.stat = TRUE show.std = TRUE, related statistic CI columns standardized coefficients shown. Fixed issue tab_model() brmsfit models longer show random effects information last update performance package. Fixed issue argument show.rownames tab_df().","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"sjplot-284","dir":"Changelog","previous_headings":"","what":"sjPlot 2.8.4","title":"sjPlot 2.8.4","text":"CRAN release: 2020-05-24","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"changes-to-functions-2-8-4","dir":"Changelog","previous_headings":"","what":"Changes to functions","title":"sjPlot 2.8.4","text":"robust estimation (argument vcov.fun tab_model() plot_model()) now also uses thus accepts estimation-types package clubSandwich. tab_model() now accepts options p.val supported parameters::model_parameters(). p.style argument tab_model() slightly revised, now also accepts \"scientific\" option scientific notation p-values. tab_model() gets digits.re argument define decimal part random effects summary. plot_models() gains value.size line.size arguments, similar plot_model(). plot_models() sort coefficients natural order now.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"bug-fixes-2-8-4","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"sjPlot 2.8.4","text":"Fixed bug plot_xtab() wrong order legend labels. Fixed bug plot_models() wrong axis title exponentiated coefficients. Fixed bug tab_model() show standard error standardized coefficients show.se = TRUE.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"sjplot-283","dir":"Changelog","previous_headings":"","what":"sjPlot 2.8.3","title":"sjPlot 2.8.3","text":"CRAN release: 2020-03-09","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"general-2-8-3","dir":"Changelog","previous_headings":"","what":"General","title":"sjPlot 2.8.3","text":"tab_model() plot_model() now support clogit models (requires latest update package insight).","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"changes-to-functions-2-8-3","dir":"Changelog","previous_headings":"","what":"Changes to functions","title":"sjPlot 2.8.3","text":"tab_model() gets p.adjust argument adjust p-values multiple comparisons. tab_model(), plot_model() plot_models() get robust-argument easily compute standard errors, confidence intervals p-values based robust estimation variance-covariance matrix. robust just convenient shortcut vcov.fun vcov.type.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"bug-fixes-2-8-3","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"sjPlot 2.8.3","text":"Fixed issue tab_model() plot_model() certain cases coefficients estimated NA. Fixed issue tab_model() collapse.ci Bayesian models. Fixed issue tab_model() p.val=\"kr\" show.df=TRUE. Fixed issue tab_model() formatting issues p-values standardized coefficients requested. Fixed issue tab_model() due changes packages sjPlot depends .","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"sjplot-282","dir":"Changelog","previous_headings":"","what":"sjPlot 2.8.2","title":"sjPlot 2.8.2","text":"CRAN release: 2020-01-23","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"function-renaming-2-8-2","dir":"Changelog","previous_headings":"","what":"Function renaming","title":"sjPlot 2.8.2","text":"sjt.itemanalysis() now named tab_itemscale(). sjt.xtab() now named tab_xtab().","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"changes-to-functions-2-8-2","dir":"Changelog","previous_headings":"","what":"Changes to functions","title":"sjPlot 2.8.2","text":"Improved handling tab_model() robust estimation general Kenward-Roger Satterthwaite approximations particular linear mixed models. Revised code cope forthcoming tidyselect-update.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"bug-fixes-2-8-2","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"sjPlot 2.8.2","text":"Improved tab_df() now uses value labels factors instead numeric values. Fixed issues related lates brms-update.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"sjplot-281","dir":"Changelog","previous_headings":"","what":"sjPlot 2.8.1","title":"sjPlot 2.8.1","text":"CRAN release: 2019-12-03","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"changes-to-functions-2-8-1","dir":"Changelog","previous_headings":"","what":"Changes to functions","title":"sjPlot 2.8.1","text":"tab_model() gets arguments bootstrap, iterations seed return bootstrapped estimates.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"bug-fixes-2-8-1","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"sjPlot 2.8.1","text":"Fixed issue tab_model() detecting labels auto.label = TRUE. Fixed issue tab_model() negative binomial hurdle mixed models (.e. glmmTMB models truncated negative-binomial family). Fixed bug tab_model() show.reflvl = TRUE. Fixed bug tab_model() labels coefficients matching correct coefficients.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"sjplot-280","dir":"Changelog","previous_headings":"","what":"sjPlot 2.8.0","title":"sjPlot 2.8.0","text":"CRAN release: 2019-11-18","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"breaking-changes-2-8-0","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"sjPlot 2.8.0","text":"Cluster functions removed, now re-implemented parameters package.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"general-2-8-0","dir":"Changelog","previous_headings":"","what":"General","title":"sjPlot 2.8.0","text":"Standardization model parameters (plot_model() tab_model()) now uses standardization based refitting model.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"changes-to-functions-2-8-0","dir":"Changelog","previous_headings":"","what":"Changes to functions","title":"sjPlot 2.8.0","text":"plot_model() gets type = \"emm\" marginal effects plot type, similar type = \"eff\". See Plotting Marginal Effects Regression Models details. verbose-argument view_df() now defaults FALSE. Updated re-arranged internal color palette, especially better behaviour selecting colors continuous palettes (see show_pals()).","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"bug-fixes-2-8-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"sjPlot 2.8.0","text":"sort.est = NULL plot_model() now preserves original order coefficients. Fixed bug automatic axis labelling plot_frq() non-labelled, numeric values. Fixed bug plot_frq() plotting factors. Arguments string.std_ci string.std_se longer ignored tab_model().","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"sjplot-272","dir":"Changelog","previous_headings":"","what":"sjPlot 2.7.2","title":"sjPlot 2.7.2","text":"CRAN release: 2019-09-29","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"general-2-7-2","dir":"Changelog","previous_headings":"","what":"General","title":"sjPlot 2.7.2","text":"Replaced performance::principal_component() parameters::principal_component(). Fixed CRAN check issues, due latest bayestestR update.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"function-renaming-2-7-2","dir":"Changelog","previous_headings":"","what":"Function renaming","title":"sjPlot 2.7.2","text":"sjp.grpfrq() now names plot_grpfrq(). sjp.xtab() now names plot_xtab().","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"changes-to-functions-2-7-2","dir":"Changelog","previous_headings":"","what":"Changes to functions","title":"sjPlot 2.7.2","text":"plot_grid() gets tags-argument add tags plot-panels.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"bug-fixes-2-7-2","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"sjPlot 2.7.2","text":"Fixed bug plot_stackfrq() data frames many missing values. Fixed bug sorting frequencies plot_frq() vector labels values. Fixed bug tab_model() show.reflvl = TRUE insert reference category first place, alphabetical order.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"sjplot-271","dir":"Changelog","previous_headings":"","what":"sjPlot 2.7.1","title":"sjPlot 2.7.1","text":"CRAN release: 2019-09-07","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"general-2-7-1","dir":"Changelog","previous_headings":"","what":"General","title":"sjPlot 2.7.1","text":"Minor revisions meet changes forthcoming update tidyr. new color palettes added (see show_sjplot_pals()).","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"changes-to-functions-2-7-1","dir":"Changelog","previous_headings":"","what":"Changes to functions","title":"sjPlot 2.7.1","text":"tab_model() now supports gamlss models. tab_df() gets digits argument, round numeric values output.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"bug-fixes-2-7-1","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"sjPlot 2.7.1","text":"Fixed bug tab_model() show.df = TRUE lmerModLmerTest. Fixed bug tab_stackfrq() items different amount valid values.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"sjplot-270","dir":"Changelog","previous_headings":"","what":"sjPlot 2.7.0","title":"sjPlot 2.7.0","text":"CRAN release: 2019-08-02","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"renamed-functions-2-7-0","dir":"Changelog","previous_headings":"","what":"Renamed functions","title":"sjPlot 2.7.0","text":"sjp.stackfrq() renamed plot_stackfrq(). sjt.stackfrq() renamed tab_stackfrq().","code":""},{"path":[]},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"plot_likert-2-7-0","dir":"Changelog","previous_headings":"Changes to functions","what":"plot_likert()","title":"sjPlot 2.7.0","text":"showed category labels top bottom legends two rows six categories. Also, categories ordered column wise instead row wise. behaviour can now controlled grouped likert plots, using group.legend.options. ordering now defaults row wise user can force categories onto single row. now automatically adjusts labels avoid overlapping.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"tab_model-2-7-0","dir":"Changelog","previous_headings":"Changes to functions","what":"tab_model()","title":"sjPlot 2.7.0","text":"now supports wbm()-models panelr-package. gets show.aicc-argument show second order AIC. gets show.reflvl-argument show reference level factors. gets string.std_se string.std_ci-argument change column header standard errors confidence intervals standardized coefficients. longer prints message default p-values mixed models based Wald approximation. show.ci50 defaults FALSE now.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"sjtitemanalysis-2-7-0","dir":"Changelog","previous_headings":"Changes to functions","what":"sjt.itemanalysis()","title":"sjPlot 2.7.0","text":"sjt.itemanalysis() now works ordered factors. clearer error message added unordered factors used. old error message helpful. factor.groups argument can now \"auto\" detect factor groups based pca Varimax rotation.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"sjpstackrq-2-7-0","dir":"Changelog","previous_headings":"Changes to functions","what":"sjp.stackrq()","title":"sjPlot 2.7.0","text":"sjp.stackfrq() renamed plot_stackfrq(). sjp.stackfrq() (now named: plot_stackfrq()) gets show.n-argument also show count values. option can combined show.prc. sjp.stackfrq() (now named: plot_stackfrq()) now also works grouped data frames.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"changes-to-other-functions-2-7-0","dir":"Changelog","previous_headings":"Changes to functions","what":"changes to other functions","title":"sjPlot 2.7.0","text":"plot_model() now supports wbm()-models panelr-package.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"bug-fixes-2-7-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"sjPlot 2.7.0","text":"plot_model(type = \"int\") now also recognized interaction terms : formula. Argument string.est tab_model() overwrite default label estimate-column-header. Minor fix tab_model() mixed models can’t compute R2. Fix issue tab_model() printing robust standard errors CI (.e. using arguments vcov*). plot_likert() option reverse.scale = TRUE resulted values = \"sum.inside\" outside way around. fixed now. view_df() mixed labels frequency values value labels present, values data. Argument wrap.labels plot_frq() properly work factor levels. Fix issue plot_models() stopped models. Fix issue sjt.stackfrq(), show.na = TRUE items zero-values.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"sjplot-263","dir":"Changelog","previous_headings":"","what":"sjPlot 2.6.3","title":"sjPlot 2.6.3","text":"CRAN release: 2019-04-27","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"general-2-6-3","dir":"Changelog","previous_headings":"","what":"General","title":"sjPlot 2.6.3","text":"Export dplyr::n(), meet changes dplyr 0.8.0. plot_model() tab_model() now support MixMod-objects package GLMMadpative, mlogit- gmnl-models.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"renamed-functions-2-6-3","dir":"Changelog","previous_headings":"","what":"Renamed functions","title":"sjPlot 2.6.3","text":"sjp.kfold_cv() renamed plot_kfold_cv(). sjp.frq() renamed plot_frq().","code":""},{"path":[]},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"tab_model-2-6-3","dir":"Changelog","previous_headings":"Changes to functions","what":"tab_model()","title":"sjPlot 2.6.3","text":"tab_model() gets show.ngrps-argument, adds back functionality print number random effects groups mixed models. tab_model() gets show.loglik-argument, adds back functionality print model’s log-Likelihood. tab_model() gets strings-argument, convenient shortcut setting column-header strings. tab_model() gets additional arguments vcov.fun, vcov.type vcov.args passed sjstats::robust(), calculate different types (clustered) robust standard errors. p.style-argument now also allows printing numeric p-values asterisks, using p.style = \"\".","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"plot_likert-2-6-3","dir":"Changelog","previous_headings":"Changes to functions","what":"plot_likert()","title":"sjPlot 2.6.3","text":"plot_likert() gets reverse.scale argument reverse order categories, positive negative values switch position. plot_likert() gets groups argument, group items plot (thanks @ndevln). Argument grid.range plot_likert() now may also vector length 2, define diffent length left right x-axis scales.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"other-2-6-3","dir":"Changelog","previous_headings":"Changes to functions","what":"Other","title":"sjPlot 2.6.3","text":"plot_frq() (former sjp.frq()) now pipe-consistent syntax, enables plotting multiple variables one function call supports grouped data frames. plot_model() gets additional arguments vcov.fun, vcov.type vcov.args passed sjstats::robust(), calculate different types (clustered) robust standard errors. sjt.xtab(), sjp.xtab(), plot_frq() sjp.grpfrq() get drop.empty()-argument, drop values / factor levels observations output.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"bug-fixes-2-6-3","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"sjPlot 2.6.3","text":"Legend labels inverted brms-models plot_model(..., type = \"diag\"). Legend labels duplicated marginal effects plots color =\"bw\" legend.title specified. Fixed encoding issues help-files. view_df() truncate frequency- percentage-values variables value labels truncated certain maximum number. tab_model() print number observations coxph-models.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"sjplot-262","dir":"Changelog","previous_headings":"","what":"sjPlot 2.6.2","title":"sjPlot 2.6.2","text":"CRAN release: 2018-12-18","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"general-2-6-2","dir":"Changelog","previous_headings":"","what":"General","title":"sjPlot 2.6.2","text":"Revised help-files vignettes.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"removed--defunct-2-6-2","dir":"Changelog","previous_headings":"","what":"Removed / Defunct","title":"sjPlot 2.6.2","text":"Following functions now defunct: sjt.lm(), sjt.glm(), sjt.lmer() sjt.glmer(). Please use tab_model() instead.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"changes-to-functions-2-6-2","dir":"Changelog","previous_headings":"","what":"Changes to functions","title":"sjPlot 2.6.2","text":"tab_model() supports printing simplex parameters monotonic effects brms models. tab_model() gets prefix.labels-argument add prefix labels categorical terms. rotation-argument sjt.pca() sjp.pca() now supports rotations psych::principal().","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"bug-fixes-2-6-2","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"sjPlot 2.6.2","text":"plot_model() longer automatically changes plot-type \"slope\" models one predictor categorical two levels. type = \"eff\" type = \"pred\" plot_model() work terms specified. robust standard errors requested tab_model(), confidence intervals p-values now re-calculated adjusted based robust standard errors. colors = \"bw\" recognized correctly plot_model(..., type = \"int\"). Fix issue sjp.frq() correct axis labels non-labelled character vectors.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"sjplot-261","dir":"Changelog","previous_headings":"","what":"sjPlot 2.6.1","title":"sjPlot 2.6.1","text":"CRAN release: 2018-10-14","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"general-2-6-1","dir":"Changelog","previous_headings":"","what":"General","title":"sjPlot 2.6.1","text":"Removed defunct functions.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"deprecated-2-6-1","dir":"Changelog","previous_headings":"","what":"Deprecated","title":"sjPlot 2.6.1","text":"sjt.lm(), sjt.glm(), sjt.lmer() sjt.glmer() now deprecated. Please use tab_model() instead.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"changes-to-functions-2-6-1","dir":"Changelog","previous_headings":"","what":"Changes to functions","title":"sjPlot 2.6.1","text":"Arguments dot.size line.size plot_model() now also apply marginal effects diagnostic plots. plot_model() now uses free x-axis scale facets models zero-inflated part. plot_model() now shows multiple plots models zero-inflated parts grids = FALSE. tab_model() gets p.style p.threshold argument indicate significance levels asteriks, determine threshold estimate considered significant. plot_model() plot_models() get p.threshold argument determine threshold estimate considered significant.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"bug-fixes-2-6-1","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"sjPlot 2.6.1","text":"Fixed bug last update made value labels disappear plot_likert(). tab_model() now also accepts multiple model-objects stored list argument, stated help-file. file-argument now works sjt.itemanalysis(). Argument show.ci tab_model() compute confidence intervals different levels.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"sjplot-260","dir":"Changelog","previous_headings":"","what":"sjPlot 2.6.0","title":"sjPlot 2.6.0","text":"CRAN release: 2018-08-23","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"general-2-6-0","dir":"Changelog","previous_headings":"","what":"General","title":"sjPlot 2.6.0","text":"sjp.scatter() revised renamed plot_scatter(). plot_scatter() pipe-friendly, also works grouped data frames. sjp.gpt() revised renamed plot_gpt(). plot_gpt() pipe-friendly, also works grouped data frames. Reduce package dependencies.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"renamed-functions-2-6-0","dir":"Changelog","previous_headings":"","what":"Renamed functions","title":"sjPlot 2.6.0","text":"sjp.scatter() renamed plot_scatter(). sjp.likert() renamed plot_likert(). sjp.gpt() renamed plot_gpt(). sjp.resid() renamed plot_residuals().","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"changes-to-functions-2-6-0","dir":"Changelog","previous_headings":"","what":"Changes to functions","title":"sjPlot 2.6.0","text":"Improved support brmsfit-objects categorical-family plot_model() tab_model(). tab_model() gets show.adj.icc-argument, also show adjusted ICC mixed models. tab_model() gets col.order-argument, reorder table columns. Argument hide.progress view_df() deprecated. Please use verbose now. statistics-argument sjt.xtab() gets \"fisher\"-option, force Fisher’s Exact Test used.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"removed--defunct-2-6-0","dir":"Changelog","previous_headings":"","what":"Removed / Defunct","title":"sjPlot 2.6.0","text":"Following functions now defunct: sjp.lm(), sjp.glm(), sjp.lmer(), sjp.glmer() sjp.int(). Please use plot_model() instead. sjt.frq(). Please use sjmisc::frq(= \"v\") instead.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"bug-fixes-2-6-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"sjPlot 2.6.0","text":"Due changes broom lmerTest packages, tidiers longer work lmerModLmerTest objects. Fix issue standardized coefficient (argument show.std) tab_model().","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"sjplot-250","dir":"Changelog","previous_headings":"","what":"sjPlot 2.5.0","title":"sjPlot 2.5.0","text":"CRAN release: 2018-07-12","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"new-functions-2-5-0","dir":"Changelog","previous_headings":"","what":"New functions","title":"sjPlot 2.5.0","text":"tab_model() replacement sjt.lm(), sjt.glm(), sjt.lmer() sjt.glmer(). Furthermore, tab_model() designed work model-objects plot_model(). New colour scales ggplot-objects: scale_fill_sjplot() scale_color_sjplot(). provide predifined colour palettes package. show_sjplot_pals() show predefined colour palettes provided package. sjplot_pal() return colour values specific palette.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"deprecated-2-5-0","dir":"Changelog","previous_headings":"","what":"Deprecated","title":"sjPlot 2.5.0","text":"Following functions now deprecated: sjp.lm(), sjp.glm(), sjp.lmer(), sjp.glmer() sjp.int(). Please use plot_model() instead. sjt.frq(). Please use sjmisc::frq(= \"v\") instead.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"removed--defunct-2-5-0","dir":"Changelog","previous_headings":"","what":"Removed / Defunct","title":"sjPlot 2.5.0","text":"Following functions now defunct: sjt.grpmean(), sjt.mwu() sjt.df(). replacements sjstats::grpmean(), sjstats::mwu() tab_df() resp. tab_dfs().","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"changes-to-functions-2-5-0","dir":"Changelog","previous_headings":"","what":"Changes to functions","title":"sjPlot 2.5.0","text":"plot_model() plot_models() get prefix.labels-argument, prefix automatically retrieved term labels either related variable name label. plot_model() gets show.zeroinf-argument show hide zero-inflation-part models plot. plot_model() gets jitter-argument add random variation data points plot types accept show.data = TRUE. plot_model() gets legend.title-argument define legend title plots display legend. plot_model() now passes arguments ... ggeffects::plot() marginal effects plots. plot_model() now plots zero-inflated part model brmsfit-objects. plot_model() now plots multivariate response models, .e. models multiple outcomes. Diagnostic plots plot_model() (type = \"diag\") can now also used brmsfit-objects. Axis limits diagnostic plots plot_model() (type = \"diag\") Stan-models (brmsfit stanreg resp. stanfit) can now set axis.lim-argument. grid.breaks-argument plot_model() plot_models() now also takes vector values directly define grid breaks plot. Better default calculation grid breaks plot_model() plot_models() grid.breaks-argument length one. terms-argument plot_model() now also allows specification range numeric values square brackets marginal effects plots, e.g. terms = \"age [30:50]\" terms = \"age [pretty]\". coefficient-plots, terms- rm.terms-arguments plot_model() now also allows specification factor levels categorical terms. Coefficients indicted factor levels kept resp. removed (see ?plot_model details). plot_model() now supports clmm-objects (package ordinal). plot_model(type = \"diag\") now also shows random-effects QQ-plots glmmTMB-models, also plots random-effects QQ-plots random effects (model one random effect term).","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"bug-fixes-2-5-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"sjPlot 2.5.0","text":"plot_model(type = \"re\") now supports standard errors confidence intervals glmmTMB-objects. Fixed typo glmmTMB-tidier, may returned wrong data zero-inflation part model. Multiple random intercepts multilevel models fitted brms area now shown facet per intercept. Remove unnecessary warning sjp.likert() uneven category count neutral category specified. plot_model(type = \"int\") automatically select mdrt.values properly non-integer variables. sjp.grpfrq() now correctly uses complete space facets facet.grid = TRUE. sjp.grpfrq(type = \"boxplot\") correctly label x-axis one category elements vector. Problems German umlauts printing HTML tables fixed.","code":""}]
+[{"path":"https://strengejacke.github.io/sjPlot/CODE_OF_CONDUCT.html","id":null,"dir":"","previous_headings":"","what":"Contributor Code of Conduct","title":"Contributor Code of Conduct","text":"contributors maintainers project, pledge respect people contribute reporting issues, posting feature requests, updating documentation, submitting pull requests patches, activities. committed making participation project harassment-free experience everyone, regardless level experience, gender, gender identity expression, sexual orientation, disability, personal appearance, body size, race, ethnicity, age, religion. Examples unacceptable behavior participants include use sexual language imagery, derogatory comments personal attacks, trolling, public private harassment, insults, unprofessional conduct. Project maintainers right responsibility remove, edit, reject comments, commits, code, wiki edits, issues, contributions aligned Code Conduct. Project maintainers follow Code Conduct may removed project team. Instances abusive, harassing, otherwise unacceptable behavior may reported opening issue contacting one project maintainers. Code Conduct adapted Contributor Covenant (http://contributor-covenant.org), version 1.0.0, available http://contributor-covenant.org/version/1/0/0/","code":""},{"path":"https://strengejacke.github.io/sjPlot/CONTRIBUTING.html","id":null,"dir":"","previous_headings":"","what":"Contributing to sjmisc","title":"Contributing to sjmisc","text":"outlines propose change sjmisc.","code":""},{"path":"https://strengejacke.github.io/sjPlot/CONTRIBUTING.html","id":"fixing-typos","dir":"","previous_headings":"","what":"Fixing typos","title":"Contributing to sjmisc","text":"Small typos grammatical errors documentation may edited directly using GitHub web interface, long changes made source file. want fix typos documentation, please edit related .R file R/ folder. edit .Rd file man/.","code":""},{"path":"https://strengejacke.github.io/sjPlot/CONTRIBUTING.html","id":"filing-an-issue","dir":"","previous_headings":"","what":"Filing an issue","title":"Contributing to sjmisc","text":"easiest way propose change new feature file issue. ’ve found bug, may also create associated issue. possible, try illustrate proposal bug minimal reproducible example.","code":""},{"path":"https://strengejacke.github.io/sjPlot/CONTRIBUTING.html","id":"pull-request","dir":"","previous_headings":"","what":"Pull request","title":"Contributing to sjmisc","text":"Please create Git branch pull request (PR). contributed code roughly follow tidyverse style guide. Exceptions guide: separated, use underscores function names, dots argument names. See example set_na(). sjmisc uses roxygen2, Markdown syntax, documentation. sjmisc uses testthat. Adding tests PR makes easier merge PR code base. PR user-visible change, may add bullet top NEWS.md describing changes made. may optionally add GitHub username, links relevant issue(s)/PR(s).","code":""},{"path":"https://strengejacke.github.io/sjPlot/CONTRIBUTING.html","id":"code-of-conduct","dir":"","previous_headings":"","what":"Code of Conduct","title":"Contributing to sjmisc","text":"Please note project released Contributor Code Conduct. participating project agree abide terms.","code":""},{"path":"https://strengejacke.github.io/sjPlot/articles/blackwhitefigures.html","id":"barplots-in-grey-scaled-colors","dir":"Articles","previous_headings":"","what":"Barplots in grey-scaled colors","title":"Black & White Figures for Print Journals","text":"two ways create plots black white greyscale. bar plots, geom.colors = \"gs\" creates plot using greyscale (based scales::grey_pal()).","code":"library(sjPlot) library(sjmisc) library(sjlabelled) library(ggplot2) theme_set(theme_bw()) data(efc) plot_grpfrq(efc$e42dep, efc$c172code, geom.colors = \"gs\")"},{"path":"https://strengejacke.github.io/sjPlot/articles/blackwhitefigures.html","id":"lineplots-in-bw-with-different-linetypes","dir":"Articles","previous_headings":"","what":"Lineplots in b/w with different linetypes","title":"Black & White Figures for Print Journals","text":"Similar barplots, lineplots - mostly plot_model() - can plotted greyscale well (colors = \"gs\"). However, cases lines colored greyscale difficult distinguish. case, plot_model() supports black & white figures different linetypes. Use colors = \"bw\" create b/w-plot.  Different linetypes apply linetyped plots, usually plot single line - ’s need different linetypes, can just set colors = \"black\" (colors = \"bw\").","code":"# create binrary response y <- ifelse(efc$neg_c_7 < median(na.omit(efc$neg_c_7)), 0, 1)  # create data frame for fitting model df <- data.frame(   y = to_factor(y),   sex = to_factor(efc$c161sex),   dep = to_factor(efc$e42dep),   barthel = efc$barthtot,   education = to_factor(efc$c172code) )  # set variable label for response set_label(df$y) <- \"High Negative Impact\"  # fit model fit <- glm(y ~., data = df, family = binomial(link = \"logit\"))  # plot marginal effects plot_model(   fit,    type = \"pred\",    terms = c(\"barthel\", \"sex\",\"dep\"),    colors = \"bw\",   ci.lvl = NA ) # plot coefficients plot_model(fit, colors = \"black\")"},{"path":"https://strengejacke.github.io/sjPlot/articles/custplot.html","id":"tweaking-plot-appearance","dir":"Articles","previous_headings":"","what":"Tweaking plot appearance","title":"Customize Plot Appearance","text":"base function globally change theme option sjp-function set_theme(). Except geom-colors geom-sizes, theme-options can set via function. new theme applied following plots created sjPlot package. various arguments change colors, sizes, angles etc. labels. Following example show changes colors, sizes, angles, geom-outlines theme.","code":"# load libraries library(sjPlot)  # for plotting library(sjmisc)  # for sample data library(ggplot2) # to access ggplot-themes  # load sample data set data(efc)  set_theme(   geom.outline.color = \"antiquewhite4\",    geom.outline.size = 1,    geom.label.size = 2,   geom.label.color = \"grey50\",   title.color = \"red\",    title.size = 1.5,    axis.angle.x = 45,    axis.textcolor = \"blue\",    base = theme_bw() )  plot_grpfrq(   efc$e42dep,    efc$e16sex,    title = NULL,    geom.colors = c(\"cadetblue\", \"coral\"),    geom.size = 0.4 )"},{"path":"https://strengejacke.github.io/sjPlot/articles/custplot.html","id":"using-the-color-brewer-palettes","dir":"Articles","previous_headings":"","what":"Using the Color Brewer palettes","title":"Customize Plot Appearance","text":"plotting functions support usage Colorbrewer palettes. apply color brewer palette, use specify palette geom.colors. valid color brewer palette recognized automatically.  overview supported color codes can obtained display.brewer.() RColorBrewer package.","code":"# blank theme set_theme(   base = theme_blank(),   axis.title.size = .9,   axis.textsize = .9,   legend.size = .7,   legend.title.size = .8,   geom.label.size = 3 )  plot_grpfrq(   efc$e42dep,    efc$e15relat,    geom.colors = \"PuRd\",    show.values = FALSE ) library(RColorBrewer) display.brewer.all()"},{"path":"https://strengejacke.github.io/sjPlot/articles/custplot.html","id":"plot-with-flipped-coordinates","dir":"Articles","previous_headings":"","what":"Plot with flipped coordinates","title":"Customize Plot Appearance","text":"plot’s axes can flipped using coord.flip = TRUE. needed, labels can placed inside bars vjust hjust arguments. cases, might need adjust label colors geom.label.color = \"white\".","code":"set_theme(geom.label.color = \"white\", geom.label.size = 3)  # labels appear very large due to export metrics plot_grpfrq(efc$e42dep, efc$e16sex, coord.flip = TRUE)"},{"path":"https://strengejacke.github.io/sjPlot/articles/custplot.html","id":"adding-plot-margins","dir":"Articles","previous_headings":"","what":"Adding plot margins","title":"Customize Plot Appearance","text":"Plots margins towards axes may look strange people (, though). restore ggplot-default behaviour, use expand.grid argument:","code":"plot_grpfrq(efc$e42dep, efc$e16sex, expand.grid = TRUE)"},{"path":"https://strengejacke.github.io/sjPlot/articles/custplot.html","id":"theme-options","dir":"Articles","previous_headings":"","what":"Theme options","title":"Customize Plot Appearance","text":"can use pre-defined theme ggplot, like theme_bw(), theme_classic() theme_minimal() default theme.","code":"set_theme(base = theme_light()) plot_frq(efc$e42dep)"},{"path":"https://strengejacke.github.io/sjPlot/articles/custplot.html","id":"pre-defined-themes","dir":"Articles","previous_headings":"","what":"Pre-defined themes","title":"Customize Plot Appearance","text":"set pre-defined themes sjPlot-package. See ?\"sjPlot-themes\" complete list.","code":"library(sjmisc) data(efc) efc <- to_factor(efc, e42dep, c172code) m <- lm(neg_c_7 ~ pos_v_4 + c12hour + e42dep + c172code, data = efc)  # reset theme set_theme(base = theme_grey())  # forest plot of regression model p <- plot_model(m)  # default theme p # pre-defined theme p + theme_sjplot()"},{"path":"https://strengejacke.github.io/sjPlot/articles/custplot.html","id":"pre-defined-scales","dir":"Articles","previous_headings":"","what":"Pre-defined scales","title":"Customize Plot Appearance","text":"also new scale ggplot-objects, scale_color_sjplot() scale_fill_sjplot().  see currently available color sets, use show_sjplot_pals().","code":"p +    theme_sjplot2() +    scale_color_sjplot(\"simply\") show_sjplot_pals()"},{"path":"https://strengejacke.github.io/sjPlot/articles/custplot.html","id":"set-up-own-themes-based-on-existing-themes","dir":"Articles","previous_headings":"","what":"Set up own themes based on existing themes","title":"Customize Plot Appearance","text":"want use specific theme base building theme, use base argument. using base instead theme, arguments settings colors etc. ignored.","code":"set_theme(base = theme_bw(), axis.linecolor = \"darkgreen\") plot_frq(efc$e42dep)"},{"path":"https://strengejacke.github.io/sjPlot/articles/custplot.html","id":"further-customization-options","dir":"Articles","previous_headings":"","what":"Further customization options","title":"Customize Plot Appearance","text":"plotting function invisibly returns ggplot-object. can add options customize appearance plot, like following example, count axis hidden (color set white):","code":"set_theme(   base = theme_classic(),   axis.tickslen = 0, # hides tick marks   axis.title.size = .9,   axis.textsize = .9,   legend.size = .7,   legend.title.size = .8,   geom.label.size = 3.5 )    plot_grpfrq(   efc$e42dep,   efc$e16sex,   coord.flip = TRUE,   show.axis.values = FALSE ) +   theme(axis.line.x = element_line(color = \"white\"))"},{"path":"https://strengejacke.github.io/sjPlot/articles/custplot.html","id":"plot-legend","dir":"Articles","previous_headings":"","what":"Plot legend","title":"Customize Plot Appearance","text":"plot’s legend can customized via various legend.-arguments, see following examples:","code":"set_theme(   base = theme_classic(),    legend.title.face = \"italic\", # title font face   legend.inside = TRUE,         # legend inside plot   legend.color = \"grey50\",      # legend label color   legend.pos = \"bottom right\",  # legend position inside plot   axis.title.size = .9,   axis.textsize = .9,   legend.size = .7,   legend.title.size = .8,   geom.label.size = 3 )  plot_grpfrq(efc$e42dep, efc$e16sex, coord.flip = TRUE) set_theme(   base = theme_classic(),    axis.linecolor = \"white\",     # \"remove\" axis lines   axis.textcolor.y = \"darkred\", # set axis label text only for y axis   axis.tickslen = 0,            # \"remove\" tick marks   legend.title.color = \"red\",   # legend title color   legend.title.size = 2,        # legend title size   legend.color = \"green\",       # legend label color   legend.pos = \"top\",           # legend position above plot   axis.title.size = .9,   axis.textsize = .9,   legend.size = .7,   geom.label.size = 3 )  plot_grpfrq(efc$e42dep, efc$e16sex)"},{"path":"https://strengejacke.github.io/sjPlot/articles/plot_interactions.html","id":"two-way-interactions","dir":"Articles","previous_headings":"","what":"Two-Way-Interactions","title":"Plotting Interaction Effects of Regression Models","text":"Note: better understand principle plotting interaction terms, might helpful read vignette marginal effects first. plot marginal effects interaction terms, least two model terms need specified (terms define interaction) terms-argument, effects computed. plot marginal effects three-way-interactions, three terms need specified terms. convenient way automatically plot interactions type = \"int\", scans model formula interaction terms uses terms-argument.  type = \"int\", terms need specified. Note plot type automatically uses first interaction term formula x-axis, second term used grouping factor. Furthermore, continuous variables used second term, can specify preset-values term mdrt.values-argument, used grouping levels. example, second term factor two levels (male/female), need choosing specific values moderator.  switch terms, example barthtot c161sex, simply switch order terms terms-argument use type = \"pred\".  switch terms plot-type type = \"int\", need re-fit model change formula accordingly, .e. using c161sex first term interaction.  default, continuous variables, minimum maximum values chosen grouping levels, 0 100 - ’s previous two plots identical. options well, e.g. mean-value +/- 1 standard deviation (suggested Cohen Cohen continuous variables popularized Aiken West 1991), can specified using mdrt.values.","code":"library(sjPlot) library(sjmisc) library(ggplot2) data(efc) theme_set(theme_sjplot())  # make categorical efc$c161sex <- to_factor(efc$c161sex)  # fit model with interaction fit <- lm(neg_c_7 ~ c12hour + barthtot * c161sex, data = efc)  plot_model(fit, type = \"pred\", terms = c(\"barthtot\", \"c161sex\")) plot_model(fit, type = \"int\") plot_model(fit, type = \"pred\", terms = c(\"c161sex\", \"barthtot [0, 100]\")) # fit model with interaction, switching terms in formula fit <- lm(neg_c_7 ~ c12hour + c161sex * barthtot, data = efc) plot_model(fit, type = \"int\") plot_model(fit, type = \"int\", mdrt.values = \"meansd\")"},{"path":"https://strengejacke.github.io/sjPlot/articles/plot_interactions.html","id":"three-way-interactions","dir":"Articles","previous_headings":"","what":"Three-Way-Interactions","title":"Plotting Interaction Effects of Regression Models","text":"Since terms-argument accepts three model terms, can also compute marginal effects 3-way-interaction.  , type = \"int\" automatically plot interaction terms, however, using mdrt.values = \"minmax\" default - case, “levels” 0 100 continuous variable barthtot chosen default.","code":"# fit model with 3-way-interaction fit <- lm(neg_c_7 ~ c12hour * barthtot * c161sex, data = efc)  # select only levels 30, 50 and 70 from continuous variable Barthel-Index plot_model(fit, type = \"pred\", terms = c(\"c12hour\", \"barthtot [30,50,70]\", \"c161sex\")) plot_model(fit, type = \"int\") #> [[1]] #>  #> [[2]] #>  #> [[3]] #>  #> [[4]]"},{"path":"https://strengejacke.github.io/sjPlot/articles/plot_interactions.html","id":"references","dir":"Articles","previous_headings":"","what":"References","title":"Plotting Interaction Effects of Regression Models","text":"Aiken West (1991). Multiple Regression: Testing Interpreting Interactions.","code":""},{"path":"https://strengejacke.github.io/sjPlot/articles/plot_marginal_effects.html","id":"marginal-effects","dir":"Articles","previous_headings":"","what":"Marginal effects","title":"Plotting Marginal Effects of Regression Models","text":"plot_model(type = \"pred\") computes predicted values possible levels values model’s predictors. simplest case, fitted model passed first argument, followed type argument term question terms argument:  plot shows predicted values response value term c12hour.","code":"library(sjPlot) library(ggplot2) data(efc) theme_set(theme_sjplot())  fit <- lm(barthtot ~ c12hour + neg_c_7 + c161sex + c172code, data = efc)  plot_model(fit, type = \"pred\", terms = \"c12hour\")"},{"path":"https://strengejacke.github.io/sjPlot/articles/plot_marginal_effects.html","id":"marginal-effects-for-different-groups","dir":"Articles","previous_headings":"Marginal effects","what":"Marginal effects for different groups","title":"Plotting Marginal Effects of Regression Models","text":"terms-argument accepts three model terms, second third term indicate grouping levels. allows predictions term question different levels model terms:  second grouping structure can defined, create plot multiple panels grid layout:","code":"plot_model(fit, type = \"pred\", terms = c(\"c12hour\", \"c172code\")) plot_model(fit, type = \"pred\", terms = c(\"c12hour\", \"c172code\", \"c161sex\"))"},{"path":"https://strengejacke.github.io/sjPlot/articles/plot_marginal_effects.html","id":"marginal-effects-at-specific-values-or-levels","dir":"Articles","previous_headings":"Marginal effects","what":"Marginal effects at specific values or levels","title":"Plotting Marginal Effects of Regression Models","text":"terms-argument defines model terms interest, model term defines grouping structure can limited certain values. allows compute plot marginal effects terms specific values . define values, put square brackets directly term name: terms = c(\"c12hour [30, 50, 80]\", \"c172code [1,3]\")  Note plot, although values 30, 50 80 selected c12hour, continuous scale automatically adds panel grids every 5 units along x-axis. Defining values especially useful variables , instance, log-transformed. plot_model() typically uses range log-transformed variable, cases want. situation, specify range terms-argument.   brackets terms-argument also accept name valid function, (back-)transform predicted valued. example, alternative specify values exponentiated, indicated [exp] terms-argument:","code":"plot_model(fit, type = \"pred\", terms = c(\"c12hour [30, 50, 80]\", \"c172code [1,3]\")) data(mtcars) mpg_model <- lm(mpg ~ log(hp), data = mtcars)  # x-values and predictions based on the log(hp)-values plot_model(mpg_model, type = \"pred\", terms = \"hp\") # x-values and predictions based on hp-values from 50 to 150 plot_model(mpg_model, type = \"pred\", terms = \"hp [50:150]\") # x-values and predictions based on exponentiated hp-values plot_model(mpg_model, type = \"pred\", terms = \"hp [exp]\")"},{"path":"https://strengejacke.github.io/sjPlot/articles/plot_marginal_effects.html","id":"polynomial-terms-and-splines","dir":"Articles","previous_headings":"Marginal effects","what":"Polynomial terms and splines","title":"Plotting Marginal Effects of Regression Models","text":"function also works models polynomial terms splines. Following code reproduces plot ?splines::bs:","code":"library(splines) data(women)  fm1 <- lm(weight ~ bs(height, df = 5), data = women) plot_model(fm1, type = \"pred\", terms = \"height\")"},{"path":"https://strengejacke.github.io/sjPlot/articles/plot_marginal_effects.html","id":"different-constant-values-for-factors","dir":"Articles","previous_headings":"Marginal effects","what":"Different constant values for factors","title":"Plotting Marginal Effects of Regression Models","text":"Model predictions based possible combinations model terms, - roughly speaking - created using expand.grid(). terms question, values used combinations. model predictors specified terms-argument, held constant (achieved sjstats::typical_value()). default, continuous variables set mean, factors set reference level.  However, one may want set factors proportions instead reference level. E.g., factor gender value 0 female value 1 male persons, set 0 marginal effects computed type = \"pred\". 40% sample female persons, another possibility hold factor constant use value .4 (reflecting proportion 40%). required, use type = \"eff\", internally call predict() compute marginal effects, rather effects::effect().","code":"data(efc) efc$c172code <- sjlabelled::as_factor(efc$c172code) fit <- lm(neg_c_7 ~ c12hour + c172code, data = efc)  # reference category is used for \"c172code\", i.e. c172code # used the first level as value for predictions plot_model(fit, type = \"pred\", terms = \"c12hour\") # proportion is used for \"c172code\", i.e. it is set to # mean(sjlabelled::as_numeric(efc$c172code), na.rm = T), # which is about 1.9715 plot_model(fit, type = \"eff\", terms = \"c12hour\")"},{"path":"https://strengejacke.github.io/sjPlot/articles/plot_marginal_effects.html","id":"interaction-terms","dir":"Articles","previous_headings":"","what":"Interaction terms","title":"Plotting Marginal Effects of Regression Models","text":"Plotting interaction terms described separate vignette.","code":""},{"path":"https://strengejacke.github.io/sjPlot/articles/plot_model_estimates.html","id":"fitting-a-logistic-regression-model","dir":"Articles","previous_headings":"","what":"Fitting a logistic regression model","title":"Plotting Estimates (Fixed Effects) of Regression Models","text":"First, fit model used following examples. examples work way model well.","code":"# create binary response y <- ifelse(efc$neg_c_7 < median(na.omit(efc$neg_c_7)), 0, 1)  # create data frame for fitting model df <- data.frame(   y = to_factor(y),   sex = to_factor(efc$c161sex),   dep = to_factor(efc$e42dep),   barthel = efc$barthtot,   education = to_factor(efc$c172code) )  # set variable label for response set_label(df$y) <- \"High Negative Impact\"  # fit model m1 <- glm(y ~., data = df, family = binomial(link = \"logit\"))"},{"path":"https://strengejacke.github.io/sjPlot/articles/plot_model_estimates.html","id":"plotting-estimates-of-generalized-linear-models","dir":"Articles","previous_headings":"","what":"Plotting estimates of generalized linear models","title":"Plotting Estimates (Fixed Effects) of Regression Models","text":"simplest function call just passing model object argument. default, estimates sorted descending order, highest effect top.  “neutral” line, .e. vertical intercept indicates effect (x-axis position 1 glm’s position 0 linear models), drawn slightly thicker grid lines. can change line color vline.color-argument.","code":"plot_model(m1) plot_model(m1, vline.color = \"red\")"},{"path":"https://strengejacke.github.io/sjPlot/articles/plot_model_estimates.html","id":"sorting-estimates","dir":"Articles","previous_headings":"","what":"Sorting estimates","title":"Plotting Estimates (Fixed Effects) of Regression Models","text":"default, estimates sorted order introduced model. Use sort.est = TRUE sort estimates descending order, highest lowest value.  Another way sort estimates use order.terms-argument. numeric vector, indicating order estimates plot. summary, see “sex2” first term, followed three dependency-categories (position 2-4), Barthel-Index (5) two levels intermediate high level education (6 7). Now want educational levels (6 7) first, gender (1), followed dependency (2-4)finally Barthel-Index (5). Use order numeric vector order.terms-argument.","code":"plot_model(m1, sort.est = TRUE) summary(m1) #>  #> Call: #> glm(formula = y ~ ., family = binomial(link = \"logit\"), data = df) #>  #> Coefficients: #>              Estimate Std. Error z value Pr(>|z|)     #> (Intercept)  0.700232   0.576715   1.214 0.224682     #> sex2         0.649136   0.186186   3.486 0.000489 *** #> dep2         0.485259   0.361498   1.342 0.179480     #> dep3         1.125130   0.361977   3.108 0.001882 **  #> dep4         0.910194   0.441774   2.060 0.039368 *   #> barthel     -0.029802   0.004732  -6.298 3.02e-10 *** #> education2   0.226525   0.200298   1.131 0.258081     #> education3   0.283600   0.249327   1.137 0.255346     #> --- #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #>  #> (Dispersion parameter for binomial family taken to be 1) #>  #>     Null deviance: 1122.16  on 814  degrees of freedom #> Residual deviance:  939.77  on 807  degrees of freedom #>   (93 observations deleted due to missingness) #> AIC: 955.77 #>  #> Number of Fisher Scoring iterations: 4 plot_model(m1, order.terms = c(6, 7, 1, 2, 3, 4, 5))"},{"path":"https://strengejacke.github.io/sjPlot/articles/plot_model_estimates.html","id":"estimates-on-the-untransformed-scale","dir":"Articles","previous_headings":"","what":"Estimates on the untransformed scale","title":"Plotting Estimates (Fixed Effects) of Regression Models","text":"default, plot_model() automatically exponentiates coefficients, appropriate (e.g. models log logit link). can explicitley prevent transformation setting transform-argument NULL, apply transformation using character vector function name.","code":"plot_model(m1, transform = NULL) plot_model(m1, transform = \"plogis\")"},{"path":"https://strengejacke.github.io/sjPlot/articles/plot_model_estimates.html","id":"showing-value-labels","dir":"Articles","previous_headings":"","what":"Showing value labels","title":"Plotting Estimates (Fixed Effects) of Regression Models","text":"default, just dots error bars plotted. Use show.values = TRUE show value labels estimates values, use show.p = FALSE suppress asterisks indicate significance level p-values. Use value.offset adjust relative positioning value labels dots lines.","code":"plot_model(m1, show.values = TRUE, value.offset = .3)"},{"path":"https://strengejacke.github.io/sjPlot/articles/plot_model_estimates.html","id":"labelling-the-plot","dir":"Articles","previous_headings":"","what":"Labelling the plot","title":"Plotting Estimates (Fixed Effects) of Regression Models","text":"seen examples, default, plotting-functions sjPlot retrieve value variable labels data labelled, using sjlabelled-package. data labelled, variable names used. cases, use arguments title, axis.labels axis.title annotate plot title axes. want variable names instead labels, even labelled data, use \"\" argument-value, e.g. axis.labels = \"\", set auto.label FALSE. Furthermore, plot_model() applies case-conversion labels default, using snakecase-package. converts labels human-readable versions. Use case = NULL turn case-conversion , refer package-vignette snakecase-package options.","code":"data(iris) m2 <- lm(Sepal.Length ~ Sepal.Width + Petal.Length + Species, data = iris)  # variable names as labels, but made \"human readable\" # separating dots are removed plot_model(m2) # to use variable names even for labelled data plot_model(m1, axis.labels = \"\", title = \"my own title\")"},{"path":"https://strengejacke.github.io/sjPlot/articles/plot_model_estimates.html","id":"pick-or-remove-specific-terms-from-plot","dir":"Articles","previous_headings":"","what":"Pick or remove specific terms from plot","title":"Plotting Estimates (Fixed Effects) of Regression Models","text":"Use terms resp. rm.terms select specific terms () plotted.","code":"# keep only coefficients sex2, dep2 and dep3 plot_model(m1, terms = c(\"sex2\", \"dep2\", \"dep3\")) # remove coefficients sex2, dep2 and dep3 plot_model(m1, rm.terms = c(\"sex2\", \"dep2\", \"dep3\"))"},{"path":"https://strengejacke.github.io/sjPlot/articles/plot_model_estimates.html","id":"standardized-estimates","dir":"Articles","previous_headings":"","what":"Standardized estimates","title":"Plotting Estimates (Fixed Effects) of Regression Models","text":"linear models, can also plot standardized beta coefficients, using type = \"std\" type = \"std2\". two options differ way coefficients standardized. type = \"std2\" plots standardized beta values, however, standardization follows Gelman’s (2008) suggestion, rescaling estimates dividing two standard deviations instead just one.","code":"plot_model(m2, type = \"std\")"},{"path":"https://strengejacke.github.io/sjPlot/articles/plot_model_estimates.html","id":"bayesian-models-fitted-with-stan","dir":"Articles","previous_headings":"","what":"Bayesian models (fitted with Stan)","title":"Plotting Estimates (Fixed Effects) of Regression Models","text":"plot_model() also supports stan-models fitted rstanarm brms packages. However, differences compared previous plot examples. First, course, confidence intervals, uncertainty intervals - high density intervals, precise. Second, ’s just one interval range, inner outer probability. default, inner probability fixed .5 (50%), outer probability specified via ci.lvl (defaults .89 (89%) Bayesian models). However, can also use arguments prob.inner prob.outer define intervals boundaries. Third, point estimate default median, can also another value, like mean. can specified bpe-argument.","code":"if (require(\"rstanarm\", quietly = TRUE)) {   # make sure we apply a nice theme   library(ggplot2)   theme_set(theme_sjplot())      data(mtcars)   m <- stan_glm(mpg ~ wt + am + cyl + gear, data = mtcars, chains = 1)      # default model   plot_model(m)   # same model, with mean point estimate, dot-style for point estimate   # and different inner/outer probabilities of the HDI   plot_model(     m,      bpe = \"mean\",     bpe.style = \"dot\",     prob.inner = .4,     prob.outer = .8   ) }"},{"path":"https://strengejacke.github.io/sjPlot/articles/plot_model_estimates.html","id":"tweaking-plot-appearance","dir":"Articles","previous_headings":"","what":"Tweaking plot appearance","title":"Plotting Estimates (Fixed Effects) of Regression Models","text":"several options customize plot appearance: colors-argument either takes name valid colorbrewer palette (see also related vignette), \"bw\" \"gs\" black/white greyscaled colors, string color name. value.offset value.size adjust positioning size value labels, shown. dot.size line.size change size dots error bars. vline.color changes neutral “intercept” line. width, alpha scale passed certain ggplot-geoms, like geom_errorbar() geom_density_ridges().","code":"plot_model(   m1,    colors = \"Accent\",    show.values = TRUE,   value.offset = .4,   value.size = 4,   dot.size = 3,   line.size = 1.5,   vline.color = \"blue\",   width = 1.5 )"},{"path":"https://strengejacke.github.io/sjPlot/articles/plot_model_estimates.html","id":"references","dir":"Articles","previous_headings":"","what":"References","title":"Plotting Estimates (Fixed Effects) of Regression Models","text":"Gelman (2008) Scaling regression inputs dividing two standard deviations. Statistics Medicine 27: 2865–2873.","code":""},{"path":"https://strengejacke.github.io/sjPlot/articles/sjtitemanalysis.html","id":"performing-an-item-analysis-of-a-scale-or-index","dir":"Articles","previous_headings":"","what":"Performing an item analysis of a scale or index","title":"Item Analysis of a Scale or an Index","text":"function performs item analysis certain statistics useful scale index development. Following statistics computed variable (column) data frame: percentage missing values mean value standard deviation skew item difficulty item discrimination Cronbach’s Alpha item removed scale mean (average) inter-item-correlation Optional, following statistics can computed well: kurstosis Shapiro-Wilk Normality Test argument factor.groups NULL, data frame df splitted groups, assuming factor.groups indicate columns (variables) data frame belong certain factor (see, instance, return value function tab_pca() parameters::principal_components() example retrieving factor groups scale). useful perfomed principal component analysis factor analysis first step, now want see whether found factors / components represent scale index score. demonstrate function, first need data:","code":""},{"path":"https://strengejacke.github.io/sjPlot/articles/sjtitemanalysis.html","id":"index-score-with-one-component","dir":"Articles","previous_headings":"","what":"Index score with one component","title":"Item Analysis of a Scale or an Index","text":"simplest function call just passing data frame argument. case, function assumes variables data frame belong one factor . Component 1 interprete output, may consider following values rule--thumbs indicating reliable scale: item difficulty range 0.2 0.8. Ideal value p+(1-p)/2 (mostly 0.5 0.8) item discrimination, acceptable values 0.2 higher; closer 1 better case total Cronbach’s Alpha value acceptable cut-0.7 (mostly index items), mean inter-item-correlation alternative measure indicate acceptability; satisfactory range lies 0.2 0.4","code":"tab_itemscale(mydf)"},{"path":"https://strengejacke.github.io/sjPlot/articles/sjtitemanalysis.html","id":"index-score-with-more-than-one-component","dir":"Articles","previous_headings":"","what":"Index score with more than one component","title":"Item Analysis of a Scale or an Index","text":"items COPE index used example represent single factor. can check , instance, principle component analysis. know, variable belongs factor (.e. variable part component), can pass numeric vector group indices argument factor.groups. case, data frame divided components specified factor.groups, component (factor) analysed. PCA extracted two components. Now tab_itemscale() … performs item analysis components, showing whether reliable useful scale index score builds index component, standardizing scale adds component-correlation-matrix, see whether index scores (based components) highly correlated . Component 1   Component 2  ","code":"library(parameters) # Compute PCA on Cope-Index, and retrieve  # factor indices for each COPE index variable pca <- parameters::principal_components(mydf) factor.groups <- parameters::closest_component(pca) tab_itemscale(mydf, factor.groups) #> Warning: Data frame needs at least three columns for reliability-test."},{"path":"https://strengejacke.github.io/sjPlot/articles/sjtitemanalysis.html","id":"adding-further-statistics","dir":"Articles","previous_headings":"","what":"Adding further statistics","title":"Item Analysis of a Scale or an Index","text":"Component 1   Component 2  ","code":"tab_itemscale(mydf, factor.groups, show.shapiro = TRUE, show.kurtosis = TRUE) #> Warning: Data frame needs at least three columns for reliability-test."},{"path":"https://strengejacke.github.io/sjPlot/articles/tab_bayes.html","id":"bayesian-models-summaries-as-html-table","dir":"Articles","previous_headings":"","what":"Bayesian models summaries as HTML table","title":"Summary of Bayesian Models as HTML Table","text":"Bayesian regression models, differences table output simple models mixed models tab_models() use Highest Density Intervals instead confidence intervals, Bayes-R-squared values, different “point estimate” (, default, median posterior draws).","code":"tab_model(m1)"},{"path":"https://strengejacke.github.io/sjPlot/articles/tab_bayes.html","id":"multivariate-response-models","dir":"Articles","previous_headings":"","what":"Multivariate response models","title":"Summary of Bayesian Models as HTML Table","text":"multivariate response models, like mediator-analysis-models, recommended print just one model table, regression displayed “model” output.","code":"tab_model(m2)"},{"path":"https://strengejacke.github.io/sjPlot/articles/tab_bayes.html","id":"show-two-credible-interval-column","dir":"Articles","previous_headings":"","what":"Show two Credible Interval-column","title":"Summary of Bayesian Models as HTML Table","text":"show second CI-column, use show.ci50 = TRUE.","code":"tab_model(m2, show.ci50 = TRUE)"},{"path":"https://strengejacke.github.io/sjPlot/articles/tab_bayes.html","id":"mixing-multivariate-and-univariate-response-models","dir":"Articles","previous_headings":"","what":"Mixing multivariate and univariate response models","title":"Summary of Bayesian Models as HTML Table","text":"multivariate univariate response models displayed one table, column Response added multivariate response model, indicate different outcomes.","code":"tab_model(m1, m2)"},{"path":"https://strengejacke.github.io/sjPlot/articles/tab_mixed.html","id":"mixed-models-summaries-as-html-table","dir":"Articles","previous_headings":"","what":"Mixed models summaries as HTML table","title":"Summary of Mixed Models as HTML Table","text":"Unlike tables non-mixed models, tab_models() adds additional information random effects table output mixed models. can hide information show.icc = FALSE show.re.var = FALSE. Furthermore, R-squared values marginal conditional R-squared statistics, based Nakagawa et al. 2017. marginal R-squared considers variance fixed effects, conditional R-squared takes fixed random effects account. p-value simple approximation, based t-statistics using normal distribution function. precise p-value can computed using p.val = \"kr\". case, applies linear mixed models, computation p-values based conditional F-tests Kenward-Roger approximation degrees freedom (using using pbkrtest-package). Note computation time consuming thus used default. can also display approximated degrees freedom show.df.","code":"m1 <- lmer(neg_c_7 ~ c160age + c161sex + e42dep + (1 | cluster), data = efc) m2 <- lmer(Reaction ~ Days + (1 + Days | Subject), data = sleepstudy)  tab_model(m1, m2) tab_model(m1, p.val = \"kr\", show.df = TRUE)"},{"path":"https://strengejacke.github.io/sjPlot/articles/tab_mixed.html","id":"generalized-linear-mixed-models","dir":"Articles","previous_headings":"","what":"Generalized linear mixed models","title":"Summary of Mixed Models as HTML Table","text":"tab_model() can also print combine models different link-functions.","code":"data(\"efc\") efc$neg_c_7d <- ifelse(efc$neg_c_7 < median(efc$neg_c_7, na.rm = TRUE), 0, 1) efc$cluster <- as.factor(efc$e15relat) m3 <- glmer(   neg_c_7d ~ c160age + c161sex + e42dep + (1 | cluster),   data = efc,    family = binomial(link = \"logit\") )  tab_model(m1, m3)"},{"path":"https://strengejacke.github.io/sjPlot/articles/tab_mixed.html","id":"more-complex-models","dir":"Articles","previous_headings":"","what":"More complex models","title":"Summary of Mixed Models as HTML Table","text":"Finally, example glmmTMB-package show easy print zero-inflated generalized linear mixed models HTML table.","code":"library(glmmTMB) data(\"Salamanders\") m4 <- glmmTMB(   count ~ spp + mined + (1 | site),   ziformula = ~ spp + mined,    family = truncated_poisson(link = \"log\"),    data = Salamanders )  tab_model(m1, m3, m4, show.ci = FALSE)"},{"path":"https://strengejacke.github.io/sjPlot/articles/tab_mixed.html","id":"references","dir":"Articles","previous_headings":"","what":"References","title":"Summary of Mixed Models as HTML Table","text":"Nakagawa S, Johnson P, Schielzeth H (2017) coefficient determination R2 intra-class correlation coefficient generalized linear mixed-effects models revisted expanded. J. R. Soc. Interface 14. doi: 10.1098/rsif.2017.0213","code":""},{"path":"https://strengejacke.github.io/sjPlot/articles/tab_model_estimates.html","id":"a-simple-html-table-from-regression-results","dir":"Articles","previous_headings":"","what":"A simple HTML table from regression results","title":"Summary of Regression Models as HTML Table","text":"First, fit two linear models demonstrate tab_model()-function. simplest way producing table output passing fitted model parameter. default, estimates, confidence intervals (CI) p-values (p) reported. summary, numbers observations well R-squared values shown.","code":"m1 <- lm(barthtot ~ c160age + c12hour + c161sex + c172code, data = efc) m2 <- lm(neg_c_7 ~ c160age + c12hour + c161sex + e17age, data = efc) tab_model(m1)"},{"path":"https://strengejacke.github.io/sjPlot/articles/tab_model_estimates.html","id":"automatic-labelling","dir":"Articles","previous_headings":"","what":"Automatic labelling","title":"Summary of Regression Models as HTML Table","text":"sjPlot-packages features labelled data, coefficients table already labelled example. name dependent variable(s) used main column header model. non-labelled data, coefficient names shown. factors involved auto.label = TRUE, “pretty” parameters names used (see format_parameters().","code":"data(mtcars) m.mtcars <- lm(mpg ~ cyl + hp + wt, data = mtcars) tab_model(m.mtcars) set.seed(2) dat <- data.frame(   y = runif(100, 0, 100),   drug = as.factor(sample(c(\"nonsense\", \"useful\", \"placebo\"), 100, TRUE)),   group = as.factor(sample(c(\"control\", \"treatment\"), 100, TRUE)) )  pretty_names <- lm(y ~ drug * group, data = dat) tab_model(pretty_names)"},{"path":"https://strengejacke.github.io/sjPlot/articles/tab_model_estimates.html","id":"turn-off-automatic-labelling","dir":"Articles","previous_headings":"Automatic labelling","what":"Turn off automatic labelling","title":"Summary of Regression Models as HTML Table","text":"turn automatic labelling, use auto.label = FALSE, provide empty character vector pred.labels dv.labels. models non-labelled data factors.","code":"tab_model(m1, auto.label = FALSE) tab_model(pretty_names, auto.label = FALSE)"},{"path":"https://strengejacke.github.io/sjPlot/articles/tab_model_estimates.html","id":"more-than-one-model","dir":"Articles","previous_headings":"","what":"More than one model","title":"Summary of Regression Models as HTML Table","text":"tab_model() can print multiple models , printed side--side. Identical coefficients matched row.","code":"tab_model(m1, m2)"},{"path":"https://strengejacke.github.io/sjPlot/articles/tab_model_estimates.html","id":"generalized-linear-models","dir":"Articles","previous_headings":"","what":"Generalized linear models","title":"Summary of Regression Models as HTML Table","text":"generalized linear models, ouput slightly adapted. Instead Estimates, column named Odds Ratios, Incidence Rate Ratios etc., depending model. coefficients case automatically converted (exponentiated). Furthermore, pseudo R-squared statistics shown summary.","code":"m3 <- glm(   tot_sc_e ~ c160age + c12hour + c161sex + c172code,    data = efc,   family = poisson(link = \"log\") )  efc$neg_c_7d <- ifelse(efc$neg_c_7 < median(efc$neg_c_7, na.rm = TRUE), 0, 1) m4 <- glm(   neg_c_7d ~ c161sex + barthtot + c172code,   data = efc,   family = binomial(link = \"logit\") )  tab_model(m3, m4)"},{"path":"https://strengejacke.github.io/sjPlot/articles/tab_model_estimates.html","id":"untransformed-estimates-on-the-linear-scale","dir":"Articles","previous_headings":"Generalized linear models","what":"Untransformed estimates on the linear scale","title":"Summary of Regression Models as HTML Table","text":"plot estimates linear scale, use transform = NULL.","code":"tab_model(m3, m4, transform = NULL, auto.label = FALSE)"},{"path":"https://strengejacke.github.io/sjPlot/articles/tab_model_estimates.html","id":"more-complex-models","dir":"Articles","previous_headings":"","what":"More complex models","title":"Summary of Regression Models as HTML Table","text":"models, like hurdle- zero-inflated models, also work tab_model(). case, zero inflation model indicated table. Use show.zeroinf = FALSE hide part table. can combine model one table.","code":"library(pscl) data(\"bioChemists\") m5 <- zeroinfl(art ~ fem + mar + kid5 + ment | kid5 + phd + ment, data = bioChemists)  tab_model(m5) tab_model(m1, m3, m5, auto.label = FALSE, show.ci = FALSE)"},{"path":"https://strengejacke.github.io/sjPlot/articles/tab_model_estimates.html","id":"show-or-hide-further-columns","dir":"Articles","previous_headings":"","what":"Show or hide further columns","title":"Summary of Regression Models as HTML Table","text":"tab_model() argument allow show hide specific columns output: show.est show/hide column model estimates. show.ci show/hide column confidence intervals. show.se show/hide column standard errors. show.std show/hide column standardized estimates (standard errors). show.p show/hide column p-values. show.stat show/hide column coefficients’ test statistics. show.df linear mixed models, p-values based degrees freedom Kenward-Rogers approximation, degrees freedom shown.","code":""},{"path":"https://strengejacke.github.io/sjPlot/articles/tab_model_estimates.html","id":"adding-columns","dir":"Articles","previous_headings":"Show or hide further columns","what":"Adding columns","title":"Summary of Regression Models as HTML Table","text":"following example, standard errors, standardized coefficients test statistics also shown.","code":"tab_model(m1, show.se = TRUE, show.std = TRUE, show.stat = TRUE)"},{"path":"https://strengejacke.github.io/sjPlot/articles/tab_model_estimates.html","id":"removing-columns","dir":"Articles","previous_headings":"Show or hide further columns","what":"Removing columns","title":"Summary of Regression Models as HTML Table","text":"following example, default columns removed.","code":"tab_model(m3, m4, show.ci = FALSE, show.p = FALSE, auto.label = FALSE)"},{"path":"https://strengejacke.github.io/sjPlot/articles/tab_model_estimates.html","id":"removing-and-sorting-columns","dir":"Articles","previous_headings":"Show or hide further columns","what":"Removing and sorting columns","title":"Summary of Regression Models as HTML Table","text":"Another way remove columns, also allows reorder columns, col.order-argument. character vector, element indicates column output. value \"est\", instance, indicates estimates, \"std.est\" column standardized estimates . default, col.order contains possible columns. columns shown (see previous tables, example using show.se = TRUE show standard errors, show.st = TRUE show standardized estimates) printed default. Colums excluded col.order shown, matter show*-arguments TRUE FALSE. show.se = TRUE, butcol.order contain element \"se\", standard errors shown. hand, show.est = FALSE, col.order include element \"est\", columns estimates shown. summary, col.order can used exclude columns table change order colums.","code":"tab_model(   m1, show.se = TRUE, show.std = TRUE, show.stat = TRUE,   col.order = c(\"p\", \"stat\", \"est\", \"std.se\", \"se\", \"std.est\") )"},{"path":"https://strengejacke.github.io/sjPlot/articles/tab_model_estimates.html","id":"collapsing-columns","dir":"Articles","previous_headings":"Show or hide further columns","what":"Collapsing columns","title":"Summary of Regression Models as HTML Table","text":"collapse.ci collapse.se, columns confidence intervals standard errors can collapsed one column together estimates. Sometimes table layout required.","code":"tab_model(m1, collapse.ci = TRUE)"},{"path":"https://strengejacke.github.io/sjPlot/articles/tab_model_estimates.html","id":"defining-own-labels","dir":"Articles","previous_headings":"","what":"Defining own labels","title":"Summary of Regression Models as HTML Table","text":"different options change labels column headers coefficients, e.g. : pred.labels change names coefficients Predictors column. Note length pred.labels must exactly match amount predictors Predictor column. dv.labels change names model columns, labelled variable labels / names dependent variables. , various string.*-arguments, change name column headings.","code":"tab_model(   m1, m2,    pred.labels = c(\"Intercept\", \"Age (Carer)\", \"Hours per Week\", \"Gender (Carer)\",                   \"Education: middle (Carer)\", \"Education: high (Carer)\",                    \"Age (Older Person)\"),   dv.labels = c(\"First Model\", \"M2\"),   string.pred = \"Coeffcient\",   string.ci = \"Conf. Int (95%)\",   string.p = \"P-Value\" )"},{"path":"https://strengejacke.github.io/sjPlot/articles/tab_model_estimates.html","id":"including-reference-level-of-categorical-predictors","dir":"Articles","previous_headings":"","what":"Including reference level of categorical predictors","title":"Summary of Regression Models as HTML Table","text":"default, categorical predictors, variable names categories regression coefficients shown table output. can include reference level categorical predictors setting show.reflvl = TRUE. show variable names, categories include reference level, also set prefix.labels = \"varname\".","code":"library(glmmTMB) data(\"Salamanders\") model <- glm(   count ~ spp + Wtemp + mined + cover,   family = poisson(),   data = Salamanders )  tab_model(model) tab_model(model, show.reflvl = TRUE) tab_model(model, show.reflvl = TRUE, prefix.labels = \"varname\")"},{"path":"https://strengejacke.github.io/sjPlot/articles/tab_model_estimates.html","id":"style-of-p-values","dir":"Articles","previous_headings":"","what":"Style of p-values","title":"Summary of Regression Models as HTML Table","text":"can change style p-values displayed argument p.style. p.style = \"stars\", p-values indicated * table. p<0.05   ** p<0.01   *** p<0.001 Another option scientific notation, using p.style = \"scientific\", also can combined digits.p.","code":"tab_model(m1, m2, p.style = \"stars\") tab_model(m1, m2, p.style = \"scientific\", digits.p = 2)"},{"path":"https://strengejacke.github.io/sjPlot/articles/tab_model_estimates.html","id":"automatic-matching-for-named-vectors","dir":"Articles","previous_headings":"Style of p-values","what":"Automatic matching for named vectors","title":"Summary of Regression Models as HTML Table","text":"Another way easily assign labels named vectors. case, doesn’t matter pred.labels labels coefficients model(s), order labels passed tab_model(). requirement labels’ names equal coefficients names appear summary()-output.","code":"# example, coefficients are \"c161sex2\" or \"c172code3\" summary(m1) #>  #> Call: #> lm(formula = barthtot ~ c160age + c12hour + c161sex + c172code,  #>     data = efc) #>  #> Residuals: #>     Min      1Q  Median      3Q     Max  #> -75.144 -14.944   4.401  18.661  72.393  #>  #> Coefficients: #>             Estimate Std. Error t value Pr(>|t|)     #> (Intercept) 87.14994    4.68009  18.621  < 2e-16 *** #> c160age     -0.20716    0.07211  -2.873  0.00418 **  #> c12hour     -0.27883    0.01865 -14.950  < 2e-16 *** #> c161sex2    -0.39402    2.08893  -0.189  0.85044     #> c172code2    1.36596    2.28440   0.598  0.55004     #> c172code3   -1.64045    2.84037  -0.578  0.56373     #> --- #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #>  #> Residual standard error: 25.35 on 815 degrees of freedom #>   (87 observations deleted due to missingness) #> Multiple R-squared:  0.2708, Adjusted R-squared:  0.2664  #> F-statistic: 60.54 on 5 and 815 DF,  p-value: < 2.2e-16  pl <- c(   `(Intercept)` = \"Intercept\",   e17age = \"Age (Older Person)\",   c160age = \"Age (Carer)\",    c12hour = \"Hours per Week\",    barthtot = \"Barthel-Index\",   c161sex2 = \"Gender (Carer)\",   c172code2 = \"Education: middle (Carer)\",    c172code3 = \"Education: high (Carer)\",   a_non_used_label = \"We don't care\" )   tab_model(   m1, m2, m3, m4,    pred.labels = pl,    dv.labels = c(\"Model1\", \"Model2\", \"Model3\", \"Model4\"),   show.ci = FALSE,    show.p = FALSE,    transform = NULL )"},{"path":"https://strengejacke.github.io/sjPlot/articles/tab_model_estimates.html","id":"keep-or-remove-coefficients-from-the-table","dir":"Articles","previous_headings":"","what":"Keep or remove coefficients from the table","title":"Summary of Regression Models as HTML Table","text":"Using terms- rm.terms-argument allows us explicitly show remove specific coefficients table output. Note names terms keep remove match coefficients names. categorical predictors, one example :","code":"tab_model(m1, terms = c(\"c160age\", \"c12hour\")) tab_model(m1, rm.terms = c(\"c172code2\", \"c161sex2\"))"},{"path":[]},{"path":"https://strengejacke.github.io/sjPlot/articles/tab_model_robust.html","id":"robust-covariance-matrix-estimation-from-model-parameters","dir":"Articles","previous_headings":"Classical Regression Models","what":"Robust Covariance Matrix Estimation from Model Parameters","title":"Robust Estimation of Standard Errors, Confidence Intervals and p-values","text":"two arguments allow choosing different methods options robust estimation: vcov.fun vcov.args. Let us start simple example, uses heteroskedasticity-consistent covariance matrix estimation estimation-type “HC3” (.e. sandwich::vcovHC(type = \"HC3\") called):","code":"data(iris) model <- lm(Petal.Length ~ Sepal.Length * Species + Sepal.Width, data = iris)  # model parameters, where SE, CI and p-values are based on robust estimation tab_model(model, vcov.fun = \"HC3\", show.se = TRUE) # compare standard errors to result from sandwich-package unname(sqrt(diag(sandwich::vcovHC(model)))) #> [1] 0.45382603 0.11884474 0.69296611 0.63031982 0.08318559 0.13045539 0.11841325"},{"path":"https://strengejacke.github.io/sjPlot/articles/tab_model_robust.html","id":"cluster-robust-covariance-matrix-estimation-sandwich","dir":"Articles","previous_headings":"Classical Regression Models","what":"Cluster-Robust Covariance Matrix Estimation (sandwich)","title":"Robust Estimation of Standard Errors, Confidence Intervals and p-values","text":"another covariance matrix estimation required, use vcov.fun-argument. argument needs suffix related vcov*()-functions value, .e. vcov.fun = \"CL\" call sandwich::vcovCL(), vcov.fun = \"HAC\" call sandwich::vcovHAC(). specific estimation type can changed vcov.args. E.g., sandwich::vcovCL() accepts estimation types HC0 HC3. next example, use clustered covariance matrix estimation HC1-estimation type. Usually, clustered covariance matrix estimation used cluster-structure data. variable indicating cluster-structure can defined sandwich::vcovCL() cluster-argument. tab_model(), additional arguments passed functions sandwich package can specified vcov.args:","code":"# change estimation-type tab_model(model, vcov.fun = \"CL\", vcov.args = list(type = \"HC1\"), show.se = TRUE) # compare standard errors to result from sandwich-package unname(sqrt(diag(sandwich::vcovCL(model)))) #> [1] 0.42197635 0.11148130 0.65274212 0.58720711 0.07934029 0.12251570 0.11058144 iris$cluster <- factor(rep(LETTERS[1:8], length.out = nrow(iris))) # change estimation-type, defining additional arguments tab_model(   model,    vcov.fun = \"CL\",    vcov.args = list(type = \"HC1\", cluster = iris$cluster),   show.se = TRUE ) # compare standard errors to result from sandwich-package unname(sqrt(diag(sandwich::vcovCL(model, cluster = iris$cluster)))) #> [1] 0.33714287 0.07192334 0.51893777 0.26415406 0.07201145 0.09661348 0.05123446"},{"path":"https://strengejacke.github.io/sjPlot/articles/tab_model_robust.html","id":"cluster-robust-covariance-matrix-estimation-clubsandwich","dir":"Articles","previous_headings":"Classical Regression Models","what":"Cluster-Robust Covariance Matrix Estimation (clubSandwich)","title":"Robust Estimation of Standard Errors, Confidence Intervals and p-values","text":"Cluster-robust estimation variance-covariance matrix can also achieved using clubSandwich::vcovCR(). Thus, vcov.fun = \"CR\", related function clubSandwich package called. Note function requires specification cluster-argument.","code":"# create fake-cluster-variable, to demonstrate cluster robust standard errors iris$cluster <- factor(rep(LETTERS[1:8], length.out = nrow(iris)))  # cluster-robust estimation tab_model(   model,    vcov.fun = \"CR1\",    vcov.args = list(cluster = iris$cluster),   show.se = TRUE ) # compare standard errors to result from clubSsandwich-package unname(sqrt(diag(clubSandwich::vcovCR(model, type = \"CR1\", cluster = iris$cluster)))) #> [1] 0.33028501 0.07046034 0.50838200 0.25878087 0.07054666 0.09464825 0.05019229"},{"path":"https://strengejacke.github.io/sjPlot/articles/tab_model_robust.html","id":"robust-covariance-matrix-estimation-on-standardized-model-parameters","dir":"Articles","previous_headings":"Classical Regression Models","what":"Robust Covariance Matrix Estimation on Standardized Model Parameters","title":"Robust Estimation of Standard Errors, Confidence Intervals and p-values","text":"Finally, robust estimation can combined standardization. However, robust covariance matrix estimation works show.std = \"std\".","code":"# model parameters, robust estimation on standardized model tab_model(   model,    show.std = \"std\",   vcov.fun = \"HC\" )"},{"path":[]},{"path":"https://strengejacke.github.io/sjPlot/articles/tab_model_robust.html","id":"robust-covariance-matrix-estimation-for-mixed-models","dir":"Articles","previous_headings":"Mixed Models","what":"Robust Covariance Matrix Estimation for Mixed Models","title":"Robust Estimation of Standard Errors, Confidence Intervals and p-values","text":"linear mixed models, definition clustered (“hierarchical” multilevel) structure data, also possible estimate cluster-robust covariance matrix. possible due clubSandwich package, thus need define arguments example.","code":"library(lme4) data(iris) set.seed(1234) iris$grp <- as.factor(sample(1:3, nrow(iris), replace = TRUE))  # fit example model model <- lme4::lmer(   Sepal.Length ~ Species * Sepal.Width + Petal.Length + (1 | grp),   data = iris )  # normal model parameters, like from 'summary()' tab_model(model) # model parameters, cluster robust estimation for mixed models tab_model(   model,    vcov.fun = \"CR1\",    vcov.args = list(cluster = iris$grp) )"},{"path":"https://strengejacke.github.io/sjPlot/articles/tab_model_robust.html","id":"robust-covariance-matrix-estimation-on-standardized-mixed-model-parameters","dir":"Articles","previous_headings":"Mixed Models","what":"Robust Covariance Matrix Estimation on Standardized Mixed Model Parameters","title":"Robust Estimation of Standard Errors, Confidence Intervals and p-values","text":", robust estimation can combined standardization linear mixed models well, cases also works show.std = \"std\".","code":"# model parameters, cluster robust estimation on standardized mixed model tab_model(   model,    show.std = \"std\",   vcov.fun = \"CR1\",    vcov.args = list(cluster = iris$grp) )"},{"path":[]},{"path":"https://strengejacke.github.io/sjPlot/articles/table_css.html","id":"export-table-as-html-file-to-open-in-word-processors","dir":"Articles","previous_headings":"Copying table output to office or word processors","what":"Export table as HTML file to open in word processors","title":"Customizing HTML tables","text":"can save HTML page file usage specifying file-argument saved HTML file can opened word processors like LibreOffice Microsoft Office.","code":""},{"path":"https://strengejacke.github.io/sjPlot/articles/table_css.html","id":"drag-and-drop-from-browser-or-rstudio-viewer-pane","dir":"Articles","previous_headings":"Copying table output to office or word processors","what":"Drag and drop from browser or RStudio viewer pane","title":"Customizing HTML tables","text":"can directly drag drop table RStudio viewer pane browser word processor. Simply select complete table mouse drag office.","code":""},{"path":"https://strengejacke.github.io/sjPlot/articles/table_css.html","id":"customizing-table-output-with-the-css-parameter","dir":"Articles","previous_headings":"","what":"Customizing table output with the CSS parameter","title":"Customizing HTML tables","text":"table output HTML format. table style (visual appearance) formatted using Cascading Style Sheets (CSS). bit familiar topics, can easily customize appearance table output. Many table elements (header, row, column, cell, summary row, first row column…) CSS-class attributes, can used change table style. Since sjt.* function well tab_model() different table elements thus different class attributes, first need know styles can customized.","code":""},{"path":"https://strengejacke.github.io/sjPlot/articles/table_css.html","id":"retrieving-customizable-styles","dir":"Articles","previous_headings":"Customizing table output with the CSS parameter","what":"Retrieving customizable styles","title":"Customizing HTML tables","text":"table functions invisibly return several values. return value page.style contains style information HTML table. can print style sheet console using cat()-function: HTML code page.content return value. following code prints HTML code table R console: Now can see table elements associated CSS class attributes.","code":"library(sjPlot) data(efc) m <- lm(barthtot ~ c160age + c12hour + c161sex + c172code, data = efc) tab <- tab_model(m) cat(tab$page.style) #> <style> #> html, body { background-color: white; } #> table { border-collapse:collapse; border:none; } #> caption { font-weight: bold; text-align:left; } #> td {  } #> .thead { border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; } #> .tdata { padding:0.2cm; text-align:left; vertical-align:top; } #> .arc { background-color:#f2f2f2; } #> .summary { padding-top:0.1cm; padding-bottom:0.1cm; } #> .summarydata { text-align:left; } #> .fixedparts { font-weight:bold; text-align:left; } #> .randomparts { font-weight:bold; text-align:left; padding-top:.8em; } #> .zeroparts { font-weight:bold; text-align:left; padding-top:.8em; } #> .simplexparts { font-weight:bold; text-align:left; padding-top:.8em; } #> .lasttablerow { border-bottom: double; } #> .firsttablerow {  } #> .firstsumrow { border-top:1px solid; } #> .labelcellborder { border-bottom:1px solid; } #> .depvarhead { text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal; } #> .depvarheadnodv { border-top: double; text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal; } #> .leftalign { text-align:left; } #> .centeralign { text-align:center; } #> .firsttablecol { text-align:left; } #> .footnote { font-style:italic; border-top:double black; text-align:right; } #> .subtitle { font-weight: normal; } #> .modelcolumn1 {  } #> .modelcolumn2 {  } #> .modelcolumn3 {  } #> .modelcolumn4 {  } #> .modelcolumn5 {  } #> .modelcolumn6 {  } #> .modelcolumn7 {  } #> .col1 {  } #> .col2 {  } #> .col3 {  } #> .col4 {  } #> .col5 {  } #> .col6 {  } #> <\/style> cat(tab$page.content) #> <table> #> <tr> #> <th class=\"thead firsttablerow firsttablecol col1\">&nbsp;<\/th> #> <th colspan=\"3\" class=\"thead firsttablerow\">Total score BARTHEL INDEX<\/th> #> <\/tr> #> <tr> #> <td class=\"depvarhead firsttablerow firsttablecol col1\">Predictors<\/td> #> <td class=\"depvarhead firsttablerow col2\">Estimates<\/td> #> <td class=\"depvarhead firsttablerow col3\">CI<\/td> #> <td class=\"depvarhead firsttablerow col4\">p<\/td> #> <\/tr> #> <tr> #> <td class=\"tdata firsttablecol col1\">(Intercept)<\/td> #> <td class=\"tdata centeralign modelcolumn1 col2\">90.06<\/td> #> <td class=\"tdata centeralign modelcolumn1 col3\">77.95&nbsp;&ndash;&nbsp;102.18<\/td> #> <td class=\"tdata centeralign modelcolumn1 col4\"><strong>&lt;0.001<\/strong><\/td> #> <\/tr> #> <tr> #> <td class=\"tdata firsttablecol col1\">carer' age<\/td> #> <td class=\"tdata centeralign modelcolumn1 col2\">&#45;0.22<\/td> #> <td class=\"tdata centeralign modelcolumn1 col3\">&#45;0.36&nbsp;&ndash;&nbsp;-0.08<\/td> #> <td class=\"tdata centeralign modelcolumn1 col4\"><strong>0.002<\/strong><\/td> #> <\/tr> #> <tr> #> <td class=\"tdata firsttablecol col1\">average number of hours<br>of care per week<\/td> #> <td class=\"tdata centeralign modelcolumn1 col2\">&#45;0.28<\/td> #> <td class=\"tdata centeralign modelcolumn1 col3\">&#45;0.31&nbsp;&ndash;&nbsp;-0.24<\/td> #> <td class=\"tdata centeralign modelcolumn1 col4\"><strong>&lt;0.001<\/strong><\/td> #> <\/tr> #> <tr> #> <td class=\"tdata firsttablecol col1\">carer's gender<\/td> #> <td class=\"tdata centeralign modelcolumn1 col2\">&#45;0.26<\/td> #> <td class=\"tdata centeralign modelcolumn1 col3\">&#45;4.36&nbsp;&ndash;&nbsp;3.83<\/td> #> <td class=\"tdata centeralign modelcolumn1 col4\">0.900<\/td> #> <\/tr> #> <tr> #> <td class=\"tdata firsttablecol col1\">carer's level of<br>education<\/td> #> <td class=\"tdata centeralign modelcolumn1 col2\">&#45;0.76<\/td> #> <td class=\"tdata centeralign modelcolumn1 col3\">&#45;3.55&nbsp;&ndash;&nbsp;2.02<\/td> #> <td class=\"tdata centeralign modelcolumn1 col4\">0.592<\/td> #> <\/tr> #> <tr> #> <td class=\"tdata leftalign summary firstsumrow\">Observations<\/td> #> <td class=\"tdata summary summarydata firstsumrow\" colspan=\"3\">821<\/td> #> <\/tr> #> <tr> #> <td class=\"tdata leftalign summary\">R<sup>2<\/sup> / R<sup>2<\/sup> adjusted<\/td> #> <td class=\"tdata summary summarydata\" colspan=\"3\">0.270 / 0.266<\/td> #> <\/tr> #>  #> <\/table>"},{"path":"https://strengejacke.github.io/sjPlot/articles/table_css.html","id":"customizing-table-output-with-the-css-parameter-1","dir":"Articles","previous_headings":"","what":"Customizing table output with the CSS parameter","title":"Customizing HTML tables","text":"can customize table output CSS parameter. parameter requires list attributes, follow certain pattern: attributes needs css. prefix followed class name (e.g. caption, thead, centeralign, etc.) equal-sign CSS format ((single) quotation marks) CSS format must end colon (;) Example: example, header row lost original style just became red. want keep original style just add additional style information, use plus-sign (+) initial character parameter attributes. following example, header row keeps original style additionally printed red:","code":"tab_model(   m,   CSS = list(     css.depvarhead = 'color: red;',     css.centeralign = 'text-align: left;',      css.firsttablecol = 'font-weight: bold;',      css.summary = 'color: blue;'   ) ) tab_model(m, CSS = list(css.depvarhead = '+color: red;'))"},{"path":"https://strengejacke.github.io/sjPlot/articles/table_css.html","id":"pre-defined-table-layouts","dir":"Articles","previous_headings":"","what":"Pre-defined Table-Layouts","title":"Customizing HTML tables","text":"pre-defined CSS-themes, can accessed css_theme()-function. pre-defined themes planned future.","code":"tab_model(m, CSS = css_theme(\"cells\"))"},{"path":"https://strengejacke.github.io/sjPlot/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Daniel Lüdecke. Author, maintainer. Alexander Bartel. Contributor. Carsten Schwemmer. Contributor. Chuck Powell. Contributor. Amir Djalovski. Contributor. Johannes Titz. Contributor.","code":""},{"path":"https://strengejacke.github.io/sjPlot/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Lüdecke D (????). sjPlot: Data Visualization Statistics Social Science. R package version 2.8.17, https://CRAN.R-project.org/package=sjPlot.","code":"@Manual{,   title = {sjPlot: Data Visualization for Statistics in Social Science},   author = {Daniel Lüdecke},   note = {R package version 2.8.17},   url = {https://CRAN.R-project.org/package=sjPlot}, }"},{"path":"https://strengejacke.github.io/sjPlot/index.html","id":"sjplot---data-visualization-for-statistics-in-social-science-","dir":"","previous_headings":"","what":"Data Visualization for Statistics in Social Science","title":"Data Visualization for Statistics in Social Science","text":"Collection plotting table output functions data visualization. Results various statistical analyses (commonly used social sciences) can visualized using package, including simple cross tabulated frequencies, histograms, box plots, (generalized) linear models, mixed effects models, PCA correlation matrices, cluster analyses, scatter plots, Likert scales, effects plots interaction terms regression models, constructing index score variables much .","code":""},{"path":[]},{"path":"https://strengejacke.github.io/sjPlot/index.html","id":"latest-development-build","dir":"","previous_headings":"Installation","what":"Latest development build","title":"Data Visualization for Statistics in Social Science","text":"install latest development snapshot (see latest changes ), type following commands R console:","code":"library(devtools) devtools::install_github(\"strengejacke/sjPlot\")"},{"path":"https://strengejacke.github.io/sjPlot/index.html","id":"official-stable-release","dir":"","previous_headings":"Installation","what":"Official, stable release","title":"Data Visualization for Statistics in Social Science","text":"install latest stable release CRAN, type following command R console:","code":"install.packages(\"sjPlot\")"},{"path":"https://strengejacke.github.io/sjPlot/index.html","id":"documentation-and-examples","dir":"","previous_headings":"","what":"Documentation and examples","title":"Data Visualization for Statistics in Social Science","text":"Please visit https://strengejacke.github.io/sjPlot/ documentation vignettes.","code":""},{"path":"https://strengejacke.github.io/sjPlot/index.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Data Visualization for Statistics in Social Science","text":"case want / cite package, please use citation('sjPlot') citation information. Since core functionality package depends ggplot-package, consider citing package well.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/dist_chisq.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot chi-squared distributions — dist_chisq","title":"Plot chi-squared distributions — dist_chisq","text":"function plots simple chi-squared distribution chi-squared distribution                shaded areas indicate chi-squared value significant p-level                reached.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/dist_chisq.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot chi-squared distributions — dist_chisq","text":"","code":"dist_chisq(   chi2 = NULL,   deg.f = NULL,   p = NULL,   xmax = NULL,   geom.colors = NULL,   geom.alpha = 0.7 )"},{"path":"https://strengejacke.github.io/sjPlot/reference/dist_chisq.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot chi-squared distributions — dist_chisq","text":"chi2 Numeric, optional. specified, chi-squared distribution deg.f degrees freedom plotted shaded area chi2 value position plotted indicates whether specified value significant . chi2 p specified, distribution without shaded area plotted. deg.f Numeric. degrees freedom chi-squared distribution. Needs specified. p Numeric, optional. specified, chi-squared distribution deg.f degrees freedom plotted shaded area position specified p-level starts plotted. chi2 p specified, distribution without shaded area plotted. xmax Numeric, optional. Specifies maximum x-axis-value. specified, x-axis ranges value p-level 0.00001 reached. geom.colors user defined color geoms. See 'Details' plot_grpfrq. geom.alpha Specifies alpha-level shaded area. Default 0.7, range 0 1.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/dist_chisq.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot chi-squared distributions — dist_chisq","text":"","code":"# a simple chi-squared distribution # for 6 degrees of freedom dist_chisq(deg.f = 6)   # a chi-squared distribution for 6 degrees of freedom, # and a shaded area starting at chi-squared value of ten. # With a df of 6, a chi-squared value of 12.59 would be \"significant\", # thus the shaded area from 10 to 12.58 is filled as \"non-significant\", # while the area starting from chi-squared value 12.59 is filled as # \"significant\" dist_chisq(chi2 = 10, deg.f = 6)   # a chi-squared distribution for 6 degrees of freedom, # and a shaded area starting at that chi-squared value, which has # a p-level of about 0.125 (which equals a chi-squared value of about 10). # With a df of 6, a chi-squared value of 12.59 would be \"significant\", # thus the shaded area from 10 to 12.58 (p-level 0.125 to p-level 0.05) # is filled as \"non-significant\", while the area starting from chi-squared # value 12.59 (p-level < 0.05) is filled as \"significant\". dist_chisq(p = 0.125, deg.f = 6)"},{"path":"https://strengejacke.github.io/sjPlot/reference/dist_f.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot F distributions — dist_f","title":"Plot F distributions — dist_f","text":"function plots simple F distribution F distribution                shaded areas indicate F value significant p-level                reached.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/dist_f.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot F distributions — dist_f","text":"","code":"dist_f(   f = NULL,   deg.f1 = NULL,   deg.f2 = NULL,   p = NULL,   xmax = NULL,   geom.colors = NULL,   geom.alpha = 0.7 )"},{"path":"https://strengejacke.github.io/sjPlot/reference/dist_f.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot F distributions — dist_f","text":"f Numeric, optional. specified, F distribution deg.f1 deg.f2 degrees freedom plotted shaded area f value position plotted indicates whether specified value significant . f p specified, distribution without shaded area plotted. deg.f1 Numeric. first degrees freedom F distribution. Needs specified. deg.f2 Numeric. second degrees freedom F distribution. Needs specified. p Numeric, optional. specified, F distribution deg.f1 deg.f2 degrees freedom plotted shaded area position specified p-level starts plotted. f p specified, distribution without shaded area plotted. xmax Numeric, optional. Specifies maximum x-axis-value. specified, x-axis ranges value p-level 0.00001 reached. geom.colors user defined color geoms. See 'Details' plot_grpfrq. geom.alpha Specifies alpha-level shaded area. Default 0.7, range 0 1.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/dist_f.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot F distributions — dist_f","text":"","code":"# a simple F distribution for 6 and 45 degrees of freedom dist_f(deg.f1 = 6, deg.f2 = 45)   # F distribution for 6 and 45 degrees of freedom, # and a shaded area starting at F value of two. # F-values equal or greater than 2.31 are \"significant\" dist_f(f = 2, deg.f1 = 6, deg.f2 = 45)   # F distribution for 6 and 45 degrees of freedom, # and a shaded area starting at a p-level of 0.2 # (F-Value about 1.5). dist_f(p = 0.2, deg.f1 = 6, deg.f2 = 45)"},{"path":"https://strengejacke.github.io/sjPlot/reference/dist_norm.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot normal distributions — dist_norm","title":"Plot normal distributions — dist_norm","text":"function plots simple normal distribution normal distribution                shaded areas indicate value significant p-level                reached.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/dist_norm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot normal distributions — dist_norm","text":"","code":"dist_norm(   norm = NULL,   mean = 0,   sd = 1,   p = NULL,   xmax = NULL,   geom.colors = NULL,   geom.alpha = 0.7 )"},{"path":"https://strengejacke.github.io/sjPlot/reference/dist_norm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot normal distributions — dist_norm","text":"norm Numeric, optional. specified, normal distribution mean sd plotted shaded area norm value position plotted indicates whether specified value significant . norm p specified, distribution without shaded area plotted. mean Numeric. Mean value normal distribution. default 0. sd Numeric. Standard deviation normal distribution. default 1. p Numeric, optional. specified, normal distribution mean sd plotted shaded area position specified p-level starts plotted. norm p specified, distribution without shaded area plotted. xmax Numeric, optional. Specifies maximum x-axis-value. specified, x-axis ranges value p-level 0.00001 reached. geom.colors user defined color geoms. See 'Details' plot_grpfrq. geom.alpha Specifies alpha-level shaded area. Default 0.7, range 0 1.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/dist_norm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot normal distributions — dist_norm","text":"","code":"# a simple normal distribution dist_norm()   # a simple normal distribution with different mean and sd. # note that curve looks similar to above plot, but axis range # has changed. dist_norm(mean = 2, sd = 4)   # a simple normal distribution dist_norm(norm = 1)   # a simple normal distribution dist_norm(p = 0.2)"},{"path":"https://strengejacke.github.io/sjPlot/reference/dist_t.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot t-distributions — dist_t","title":"Plot t-distributions — dist_t","text":"function plots simple t-distribution t-distribution                shaded areas indicate t-value significant p-level                reached.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/dist_t.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot t-distributions — dist_t","text":"","code":"dist_t(   t = NULL,   deg.f = NULL,   p = NULL,   xmax = NULL,   geom.colors = NULL,   geom.alpha = 0.7 )"},{"path":"https://strengejacke.github.io/sjPlot/reference/dist_t.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot t-distributions — dist_t","text":"t Numeric, optional. specified, t-distribution deg.f degrees freedom plotted shaded area t value position plotted indicates whether specified value significant . t p specified, distribution without shaded area plotted. deg.f Numeric. degrees freedom t-distribution. Needs specified. p Numeric, optional. specified, t-distribution deg.f degrees freedom plotted shaded area position specified p-level starts plotted. t p specified, distribution without shaded area plotted. xmax Numeric, optional. Specifies maximum x-axis-value. specified, x-axis ranges value p-level 0.00001 reached. geom.colors user defined color geoms. See 'Details' plot_grpfrq. geom.alpha Specifies alpha-level shaded area. Default 0.7, range 0 1.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/dist_t.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot t-distributions — dist_t","text":"","code":"# a simple t-distribution # for 6 degrees of freedom dist_t(deg.f = 6)   # a t-distribution for 6 degrees of freedom, # and a shaded area starting at t-value of one. # With a df of 6, a t-value of 1.94 would be \"significant\". dist_t(t = 1, deg.f = 6)   # a t-distribution for 6 degrees of freedom, # and a shaded area starting at p-level of 0.4 # (t-value of about 0.26). dist_t(p = 0.4, deg.f = 6)"},{"path":"https://strengejacke.github.io/sjPlot/reference/efc.html","id":null,"dir":"Reference","previous_headings":"","what":"Sample dataset from the EUROFAMCARE project — efc","title":"Sample dataset from the EUROFAMCARE project — efc","text":"SPSS sample data set, imported read_spss function.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_frq.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot frequencies of variables — plot_frq","title":"Plot frequencies of variables — plot_frq","text":"Plot frequencies variable bar graph, histogram, box plot etc.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_frq.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot frequencies of variables — plot_frq","text":"","code":"plot_frq(   data,   ...,   title = \"\",   weight.by = NULL,   title.wtd.suffix = NULL,   sort.frq = c(\"none\", \"asc\", \"desc\"),   type = c(\"bar\", \"dot\", \"histogram\", \"line\", \"density\", \"boxplot\", \"violin\"),   geom.size = NULL,   geom.colors = \"#336699\",   errorbar.color = \"darkred\",   axis.title = NULL,   axis.labels = NULL,   xlim = NULL,   ylim = NULL,   wrap.title = 50,   wrap.labels = 20,   grid.breaks = NULL,   expand.grid = FALSE,   show.values = TRUE,   show.n = TRUE,   show.prc = TRUE,   show.axis.values = TRUE,   show.ci = FALSE,   show.na = FALSE,   show.mean = FALSE,   show.mean.val = TRUE,   show.sd = TRUE,   drop.empty = TRUE,   mean.line.type = 2,   mean.line.size = 0.5,   inner.box.width = 0.15,   inner.box.dotsize = 3,   normal.curve = FALSE,   normal.curve.color = \"red\",   normal.curve.size = 0.8,   normal.curve.alpha = 0.4,   auto.group = NULL,   coord.flip = FALSE,   vjust = \"bottom\",   hjust = \"center\",   y.offset = NULL )"},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_frq.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot frequencies of variables — plot_frq","text":"data data frame, grouped data frame. ... Optional, unquoted names variables selected processing. Required, data data frame (vector) selected variables data processed. may also use functions like : tidyselect's select_helpers. title Character vector, used plot title. default, response_labels called retrieve label dependent variable, used title. Use title = \"\" remove title. weight.Vector weights applied weight cases. Must vector length input vector. Default NULL, weights used. title.wtd.suffix Suffix (string) title, weight.specified, e.g. title.wtd.suffix=\" (weighted)\". Default NULL, title suffix cases weighted. sort.frq Determines whether categories sorted according frequencies . Default \"none\", categories sorted frequency. Use \"asc\" \"desc\" sorting categories ascending descending order. type Specifies plot type. May abbreviated. \"bar\" simple bars (default) \"dot\" dot plot \"histogram\" histogram (apply grouped frequencies) \"line\" line-styled histogram filled area \"density\" density plot (apply grouped frequencies) \"boxplot\" box plot \"violin\" violin plots geom.size size resp. width geoms (bar width, line thickness point size, depending plot type function). Note bar bin widths mostly need smaller values dot sizes. geom.colors User defined color geoms, e.g. geom.colors = \"#0080ff\". errorbar.color Color confidence interval bars (error bars). applies type = \"bar\". case dot plots, error bars colors dots (see geom.colors). axis.title Character vector length one two (depending plot function type), used title(s) x y axis. specified, default labelling  chosen. Note: plot types support argument. cases, use return value add axis titles manually labs, e.g.: $plot.list[[1]] + labs(x = ...) axis.labels character vector labels used axis labels. Optional argument, since cases, axis labels set automatically. xlim Numeric vector length two, defining lower upper axis limits x scale. default, argument set NULL, .e. x-axis fits required range data. ylim numeric vector length two, defining lower upper axis limits y scale. default, argument set NULL, .e. y-axis fits required range data. wrap.title Numeric, determines many chars plot title displayed one line line break inserted. wrap.labels numeric, determines many chars value, variable axis labels displayed one line line break inserted. grid.breaks numeric; sets distance breaks axis, .e. every grid.breaks'th position major grid printed. expand.grid logical, TRUE, plot grid expanded, .e. small margin axes plotting region. Default FALSE. show.values Logical, whether values plotted . show.n logical, TRUE, adds total number cases group category labels. show.prc logical, TRUE (default), percentage values plotted bar FALSE, percentage values removed. show.axis.values logical, whether category, count percentage values axis printed . show.ci Logical, TRUE), adds notches box plot, used compare groups; notches two boxes overlap, medians considered significantly different. show.na logical, TRUE, NA's (missing values) added output. show.mean Logical, TRUE, vertical line histograms drawn indicate mean value variables. applies histogram-charts. show.mean.val Logical, TRUE (default), mean value printed vertical line indicates variable's mean. applies histogram-charts. show.sd Logical, TRUE, standard deviation annotated shaded rectangle around mean intercept line. applies histogram-charts. drop.empty Logical, TRUE variable's values labeled, values / factor levels occurrence data omitted output. FALSE, labeled values observations still printed table (frequency 0). mean.line.type Numeric value, indicating linetype mean intercept line. applies histogram-charts show.mean = TRUE. mean.line.size Numeric, size mean intercept line. applies histogram-charts show.mean = TRUE. inner.box.width width inner box plot plotted inside violin plots. applies type = \"violin\". Default value 0.15 inner.box.dotsize size mean dot insie violin box plot. Applies type = \"violin\" \"boxplot\". normal.curve Logical, TRUE, normal curve, adjusted data, plotted histogram density plot. Default FALSE. applies histograms density plots plotted (see type). normal.curve.color Color normal curve line. applies normal.curve = TRUE. normal.curve.size Numeric, size normal curve line. applies normal.curve = TRUE. normal.curve.alpha Transparancy level (alpha value) normal curve. applies normal.curve = TRUE. auto.group numeric value, indicating minimum amount unique values count variable, automatic grouping smaller units done (see group_var). Default value auto.group NULL, .e. auto-grouping . See group_var examples grouping. coord.flip logical, TRUE, x y axis swapped. vjust character vector, indicating vertical position value labels. Allowed values vjust aesthetics ggplot2: \"left\", \"center\", \"right\", \"bottom\", \"middle\", \"top\" new options like \"inward\" \"outward\", align text towards away center plot respectively. hjust character vector, indicating horizontal position value labels. Allowed values vjust aesthetics ggplot2: \"left\", \"center\", \"right\", \"bottom\", \"middle\", \"top\" new options like \"inward\" \"outward\", align text towards away center plot respectively. y.offset numeric, offset text labels alignment adjusted top/bottom geom (see hjust vjust).","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_frq.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot frequencies of variables — plot_frq","text":"ggplot-object.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_frq.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Plot frequencies of variables — plot_frq","text":"function works variables integer values (numeric         factor levels), .e. scales / centered variables         fractional part may result unexpected behaviour.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_frq.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot frequencies of variables — plot_frq","text":"","code":"library(sjlabelled) data(efc) data(iris)  # simple plots, two different notations plot_frq(iris, Species)  plot_frq(efc$tot_sc_e)   # boxplot plot_frq(efc$e17age, type = \"box\")   if (require(\"dplyr\")) {   # histogram, pipe-workflow   efc %>%     dplyr::select(e17age, c160age) %>%     plot_frq(type = \"hist\", show.mean = TRUE)    # bar plot(s)   plot_frq(efc, e42dep, c172code) } #> Loading required package: dplyr #>  #> Attaching package: ‘dplyr’ #> The following object is masked from ‘package:sjlabelled’: #>  #>     as_label #> The following objects are masked from ‘package:stats’: #>  #>     filter, lag #> The following objects are masked from ‘package:base’: #>  #>     intersect, setdiff, setequal, union #> [[1]]  #>  #> [[2]]  #>   if (require(\"dplyr\") && require(\"gridExtra\")) {   # grouped data frame, all panels in one plot   efc %>%     group_by(e42dep) %>%     plot_frq(c161sex) %>%     plot_grid() } #> Loading required package: gridExtra #>  #> Attaching package: ‘gridExtra’ #> The following object is masked from ‘package:dplyr’: #>  #>     combine   # \\donttest{ library(sjmisc) # grouped variable ageGrp <- group_var(efc$e17age) ageGrpLab <- group_labels(efc$e17age) plot_frq(ageGrp, title = get_label(efc$e17age), axis.labels = ageGrpLab)   # plotting confidence intervals. expand grid and v/hjust for text labels plot_frq(   efc$e15relat, type = \"dot\", show.ci = TRUE, sort.frq = \"desc\",   coord.flip = TRUE, expand.grid = TRUE, vjust = \"bottom\", hjust = \"left\" )   # histogram with overlayed normal curve plot_frq(efc$c160age, type = \"h\", show.mean = TRUE, show.mean.val = TRUE,         normal.curve = TRUE, show.sd = TRUE, normal.curve.color = \"blue\",         normal.curve.size = 3, ylim = c(0,50)) #> Warning: Removed 4 rows containing missing values or values outside the scale range #> (`geom_bar()`).  # }"},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_gpt.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot grouped proportional tables — plot_gpt","title":"Plot grouped proportional tables — plot_gpt","text":"Plot grouped proportional crosstables, proportion                level x highest category y                plotted, subgroup grp.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_gpt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot grouped proportional tables — plot_gpt","text":"","code":"plot_gpt(   data,   x,   y,   grp,   colors = \"metro\",   geom.size = 2.5,   shape.fill.color = \"#f0f0f0\",   shapes = c(15, 16, 17, 18, 21, 22, 23, 24, 25, 7, 8, 9, 10, 12),   title = NULL,   axis.labels = NULL,   axis.titles = NULL,   legend.title = NULL,   legend.labels = NULL,   wrap.title = 50,   wrap.labels = 15,   wrap.legend.title = 20,   wrap.legend.labels = 20,   axis.lim = NULL,   grid.breaks = NULL,   show.total = TRUE,   annotate.total = TRUE,   show.p = TRUE,   show.n = TRUE )"},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_gpt.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot grouped proportional tables — plot_gpt","text":"data data frame, grouped data frame. x Categorical variable, proportion category x highest category y printed along x-axis. y Categorical numeric variable. binary variable, y recoded binary variable, dichtomized highest category remaining categories. grp Grouping variable, define y-axis colors May character vector color values hex-format, valid color value names (see demo(\"colors\")) name pre-defined color palette. Following options valid colors argument: specified, default color brewer palette used, suitable plot style. \"gs\", greyscale used. \"bw\", plot-type line-plot, plot black/white uses different line types distinguish groups (see package-vignette). colors valid color brewer palette name, related palette used. Use RColorBrewer::display.brewer.() view available palette names. pre-defined color palettes package, see sjPlot-themes details. Else specify color values names vector (e.g. colors = \"#00ff00\" colors = c(\"firebrick\", \"blue\")). geom.size size resp. width geoms (bar width, line thickness point size, depending plot type function). Note bar bin widths mostly need smaller values dot sizes. shape.fill.color Optional color vector, fill-color non-filled shapes shapes Numeric vector shape styles, used map different categories x. title Character vector, used plot title. default, response_labels called retrieve label dependent variable, used title. Use title = \"\" remove title. axis.labels character vector labels used axis labels. Optional argument, since cases, axis labels set automatically. axis.titles character vector length one two, defining title(s) x-axis y-axis. legend.title Character vector, used legend title plots legend. legend.labels character vector labels guide/legend. wrap.title Numeric, determines many chars plot title displayed one line line break inserted. wrap.labels numeric, determines many chars value, variable axis labels displayed one line line break inserted. wrap.legend.title numeric, determines many chars legend's title displayed one line line break inserted. wrap.legend.labels numeric, determines many chars legend labels displayed one line line break inserted. axis.lim Numeric vector length 2, defining range plot axis. Depending plot type, may effect either x- y-axis, . multiple plot outputs (e.g., type = \"eff\" type = \"slope\" plot_model), axis.lim may also list vectors length 2, defining axis limits plot (non-faceted). grid.breaks numeric; sets distance breaks axis, .e. every grid.breaks'th position major grid printed. show.total Logical, TRUE, total summary line aggregated grp added. annotate.total Logical, TRUE show.total = TRUE, total-row figure highlighted slightly shaded background. show.p Logical, adds significance levels values, value variable labels. show.n logical, TRUE, adds total number cases group category labels.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_gpt.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot grouped proportional tables — plot_gpt","text":"ggplot-object.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_gpt.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plot grouped proportional tables — plot_gpt","text":"p-values based chisq.test x            y grp.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_gpt.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot grouped proportional tables — plot_gpt","text":"","code":"if (requireNamespace(\"haven\")) {   data(efc)    # the proportion of dependency levels in female   # elderly, for each family carer's relationship   # to elderly   plot_gpt(efc, e42dep, e16sex, e15relat)    # proportion of educational levels in highest   # dependency category of elderly, for different   # care levels   plot_gpt(efc, c172code, e42dep, n4pstu) }"},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_grid.html","id":null,"dir":"Reference","previous_headings":"","what":"Arrange list of plots as grid — plot_grid","title":"Arrange list of plots as grid — plot_grid","text":"Plot multiple ggplot-objects grid-arranged single plot.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_grid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Arrange list of plots as grid — plot_grid","text":"","code":"plot_grid(x, margin = c(1, 1, 1, 1), tags = NULL)"},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_grid.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Arrange list of plots as grid — plot_grid","text":"x list ggplot-objects. See 'Details'. margin numeric vector length 4, indicating top, right, bottom left margin plot, centimetres. tags Add tags subfigures. Can TRUE (letter tags) character vector containing tags labels.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_grid.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Arrange list of plots as grid — plot_grid","text":"object class gtable.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_grid.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Arrange list of plots as grid — plot_grid","text":"function takes list ggplot-objects argument.          Plotting functions package produce multiple plot          objects (e.g., argument facet.grid) usually          return multiple plots list (return value named plot.list).          arrange plots grid single plot, use plot_grid.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_grid.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Arrange list of plots as grid — plot_grid","text":"","code":"if (require(\"dplyr\") && require(\"gridExtra\")) {   library(ggeffects)   data(efc)    # fit model   fit <- glm(     tot_sc_e ~ c12hour + e17age + e42dep + neg_c_7,     data = efc,     family = poisson   )    # plot marginal effects for each predictor, each as single plot   p1 <- ggpredict(fit, \"c12hour\") %>%     plot(show_y_title = FALSE, show_title = FALSE)   p2 <- ggpredict(fit, \"e17age\") %>%     plot(show_y_title = FALSE, show_title = FALSE)   p3 <- ggpredict(fit, \"e42dep\") %>%     plot(show_y_title = FALSE, show_title = FALSE)   p4 <- ggpredict(fit, \"neg_c_7\") %>%     plot(show_y_title = FALSE, show_title = FALSE)    # plot grid   plot_grid(list(p1, p2, p3, p4))    # plot grid   plot_grid(list(p1, p2, p3, p4), tags = TRUE) } #>  #> Attaching package: ‘ggeffects’ #> The following object is masked _by_ ‘.GlobalEnv’: #>  #>     efc"},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_grpfrq.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot grouped or stacked frequencies — plot_grpfrq","title":"Plot grouped or stacked frequencies — plot_grpfrq","text":"Plot grouped stacked frequencies variables bar/dot,                box violin plots, line plot.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_grpfrq.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot grouped or stacked frequencies — plot_grpfrq","text":"","code":"plot_grpfrq(   var.cnt,   var.grp,   type = c(\"bar\", \"dot\", \"line\", \"boxplot\", \"violin\"),   bar.pos = c(\"dodge\", \"stack\"),   weight.by = NULL,   intr.var = NULL,   title = \"\",   title.wtd.suffix = NULL,   legend.title = NULL,   axis.titles = NULL,   axis.labels = NULL,   legend.labels = NULL,   intr.var.labels = NULL,   wrap.title = 50,   wrap.labels = 15,   wrap.legend.title = 20,   wrap.legend.labels = 20,   geom.size = NULL,   geom.spacing = 0.15,   geom.colors = \"Paired\",   show.values = TRUE,   show.n = TRUE,   show.prc = TRUE,   show.axis.values = TRUE,   show.ci = FALSE,   show.grpcnt = FALSE,   show.legend = TRUE,   show.na = FALSE,   show.summary = FALSE,   drop.empty = TRUE,   auto.group = NULL,   ylim = NULL,   grid.breaks = NULL,   expand.grid = FALSE,   inner.box.width = 0.15,   inner.box.dotsize = 3,   smooth.lines = FALSE,   emph.dots = TRUE,   summary.pos = \"r\",   facet.grid = FALSE,   coord.flip = FALSE,   y.offset = NULL,   vjust = \"bottom\",   hjust = \"center\" )"},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_grpfrq.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot grouped or stacked frequencies — plot_grpfrq","text":"var.cnt Vector counts, frequencies means plotted printed. var.grp Factor cross-classifying variable, var.cnt grouped categories represented var.grp. type Specifies plot type. May abbreviated. \"bar\" simple bars (default) \"dot\" dot plot \"histogram\" histogram (apply grouped frequencies) \"line\" line-styled histogram filled area \"density\" density plot (apply grouped frequencies) \"boxplot\" box plot \"violin\" violin plots bar.pos Indicates whether bars positioned side--side (default), stacked (bar.pos = \"stack\"). May abbreviated. weight.Vector weights applied weight cases. Must vector length input vector. Default NULL, weights used. intr.var interaction variable can used box plots. Divides category indicated var.grp factors intr.var, category var.grp subgrouped intr.var's categories. applies type = \"boxplot\" type = \"violin\". title character vector, used plot title. Depending plot type function, set automatically. title = \"\", title printed. effect-plots, may also character vector length > 1, define titles sub-plot facet. title.wtd.suffix Suffix (string) title, weight.specified, e.g. title.wtd.suffix=\" (weighted)\". Default NULL, title suffix cases weighted. legend.title character vector, used title plot legend. axis.titles character vector length one two, defining title(s) x-axis y-axis. axis.labels character vector labels used axis labels. Optional argument, since cases, axis labels set automatically. legend.labels character vector labels guide/legend. intr.var.labels character vector labels x-axis breaks interaction variables included. labels replace axis.labels. applies, using box violin plots (.e. type = \"boxplot\" \"violin\") intr.var NULL. wrap.title numeric, determines many chars plot title displayed one line line break inserted. wrap.labels numeric, determines many chars value, variable axis labels displayed one line line break inserted. wrap.legend.title numeric, determines many chars legend's title displayed one line line break inserted. wrap.legend.labels numeric, determines many chars legend labels displayed one line line break inserted. geom.size size resp. width geoms (bar width, line thickness point size, depending plot type function). Note bar bin widths mostly need smaller values dot sizes. geom.spacing spacing geoms (.e. bar spacing) geom.colors user defined color geoms. See 'Details' plot_grpfrq. show.values Logical, whether values plotted . show.n logical, TRUE, adds total number cases group category labels. show.prc logical, TRUE (default), percentage values plotted bar FALSE, percentage values removed. show.axis.values logical, whether category, count percentage values axis printed . show.ci Logical, TRUE), adds notches box plot, used compare groups; notches two boxes overlap, medians considered significantly different. show.grpcnt logical, TRUE, count within group added category labels (e.g. \"Cat 1 (n=87)\"). Default value FALSE. show.legend logical, TRUE, depending plot type function, legend added plot. show.na logical, TRUE, NA's (missing values) added output. show.summary logical, TRUE (default), summary chi-squared statistics (see chisq.test), Cramer's V Phi-value etc. shown. cell contains expected values lower five (lower 10 df 1), Fisher's exact test (see fisher.test) computed instead chi-squared test. table's matrix larger 2x2, Fisher's exact test Monte Carlo simulation computed. drop.empty Logical, TRUE variable's values labeled, values / factor levels occurrence data omitted output. FALSE, labeled values observations still printed table (frequency 0). auto.group numeric value, indicating minimum amount unique values count variable, automatic grouping smaller units done (see group_var). Default value auto.group NULL, .e. auto-grouping . See group_var examples grouping. ylim numeric vector length two, defining lower upper axis limits y scale. default, argument set NULL, .e. y-axis fits required range data. grid.breaks numeric; sets distance breaks axis, .e. every grid.breaks'th position major grid printed. expand.grid logical, TRUE, plot grid expanded, .e. small margin axes plotting region. Default FALSE. inner.box.width width inner box plot plotted inside violin plots. applies type = \"violin\". Default value 0.15 inner.box.dotsize size mean dot insie violin box plot. Applies type = \"violin\" \"boxplot\". smooth.lines prints smooth line curve. applies, argument type = \"line\". emph.dots logical, TRUE, groups dots dot-plot highlighted shaded rectangle. summary.pos position model summary printed show.summary TRUE. Default \"r\", .e. printed upper right corner. Use \"l\" upper left corner. facet.grid TRUE arrange lay multiple plots grid integrated single plot. argument calls facet_wrap facet_grid arrange plots. Use plot_grid plot multiple plot-objects arranged grid grid.arrange. coord.flip logical, TRUE, x y axis swapped. y.offset numeric, offset text labels alignment adjusted top/bottom geom (see hjust vjust). vjust character vector, indicating vertical position value labels. Allowed values vjust aesthetics ggplot2: \"left\", \"center\", \"right\", \"bottom\", \"middle\", \"top\" new options like \"inward\" \"outward\", align text towards away center plot respectively. hjust character vector, indicating horizontal position value labels. Allowed values vjust aesthetics ggplot2: \"left\", \"center\", \"right\", \"bottom\", \"middle\", \"top\" new options like \"inward\" \"outward\", align text towards away center plot respectively.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_grpfrq.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot grouped or stacked frequencies — plot_grpfrq","text":"ggplot-object.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_grpfrq.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plot grouped or stacked frequencies — plot_grpfrq","text":"geom.colors may character vector color values          hex-format, valid color value names (see demo(\"colors\")          name color brewer palette.          Following options valid geom.colors argument: specified, default color brewer palette used, suitable plot style (.e. diverging likert scales, qualitative grouped bars etc.). \"gs\", greyscale used. \"bw\", plot-type line-plot, plot black/white uses different line types distinguish groups (see package-vignette). geom.colors valid color brewer palette name, related palette used. Use RColorBrewer::display.brewer.() view available palette names. Else specify color values names vector (e.g. geom.colors = c(\"#f00000\", \"#00ff00\")).","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_grpfrq.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot grouped or stacked frequencies — plot_grpfrq","text":"","code":"data(efc) plot_grpfrq(efc$e17age, efc$e16sex, show.values = FALSE)   # boxplot plot_grpfrq(efc$e17age, efc$e42dep, type = \"box\") #> Warning: Solution may be nonunique #> Warning: Solution may be nonunique #> Warning: Solution may be nonunique #> Warning: Solution may be nonunique #> Warning: Solution may be nonunique #> Warning: Solution may be nonunique   # grouped bars plot_grpfrq(efc$e42dep, efc$e16sex, title = NULL)   # box plots with interaction variable plot_grpfrq(efc$e17age, efc$e42dep, intr.var = efc$e16sex, type = \"box\") #> Warning: Solution may be nonunique #> Warning: Solution may be nonunique #> Warning: Solution may be nonunique   # Grouped bar plot plot_grpfrq(efc$neg_c_7, efc$e42dep, show.values = FALSE)   # same data as line plot plot_grpfrq(efc$neg_c_7, efc$e42dep, type = \"line\")   # show ony categories where we have data (i.e. drop zero-counts) library(dplyr) efc <- dplyr::filter(efc, e42dep %in% c(3,4)) plot_grpfrq(efc$c161sex, efc$e42dep, drop.empty = TRUE)   # show all categories, even if not in data plot_grpfrq(efc$c161sex, efc$e42dep, drop.empty = FALSE)"},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_kfold_cv.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot model fit from k-fold cross-validation — plot_kfold_cv","title":"Plot model fit from k-fold cross-validation — plot_kfold_cv","text":"function plots aggregated residuals k-fold cross-validated   models outcome. allows evaluate model performs   according - underestimation outcome.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_kfold_cv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot model fit from k-fold cross-validation — plot_kfold_cv","text":"","code":"plot_kfold_cv(data, formula, k = 5, fit)"},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_kfold_cv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot model fit from k-fold cross-validation — plot_kfold_cv","text":"data data frame, used split data k training-test-pairs. formula model formula, used fit linear models (lm) k training data sets. Use fit specify fitted model (also models linear models), used compute cross validation. fit missing, formula ignored. k Number folds. fit Model object, used compute cross validation. fit missing, formula ignored. Currently, linear, poisson negative binomial regression models supported.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_kfold_cv.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plot model fit from k-fold cross-validation — plot_kfold_cv","text":"function, first, generates k cross-validated test-training            pairs            fits model, specified formula- fit-            argument, training data sets.             , test data used predict outcome            models fit training data, residuals            test data plotted observed values (outcome)            test data (note: poisson negative binomial models,            deviance residuals calculated). plot can used validate model            see, whether - (residuals > 0) underestimates            (residuals < 0) model's outcome.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_kfold_cv.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Plot model fit from k-fold cross-validation — plot_kfold_cv","text":"Currently, linear, poisson negative binomial regression models supported.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_kfold_cv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot model fit from k-fold cross-validation — plot_kfold_cv","text":"","code":"data(efc)  plot_kfold_cv(efc, neg_c_7 ~ e42dep + c172code + c12hour) #> `geom_smooth()` using formula = 'y ~ x' #> Warning: Removed 117 rows containing non-finite outside the scale range #> (`stat_smooth()`). #> Warning: Removed 117 rows containing missing values or values outside the scale range #> (`geom_point()`).  plot_kfold_cv(mtcars, mpg ~.) #> `geom_smooth()` using formula = 'y ~ x'   # for poisson models. need to fit a model and use 'fit'-argument fit <- glm(tot_sc_e ~ neg_c_7 + c172code, data = efc, family = poisson) plot_kfold_cv(efc, fit = fit) #> `geom_smooth()` using formula = 'y ~ x' #> Warning: pseudoinverse used at -0.045 #> Warning: neighborhood radius 1.045 #> Warning: reciprocal condition number  0 #> Warning: There are other near singularities as well. 1 #> Warning: pseudoinverse used at -0.045 #> Warning: neighborhood radius 1.045 #> Warning: reciprocal condition number  0 #> Warning: There are other near singularities as well. 1   # and for negative binomial models fit <- MASS::glm.nb(tot_sc_e ~ neg_c_7 + c172code, data = efc) plot_kfold_cv(efc, fit = fit) #> `geom_smooth()` using formula = 'y ~ x' #> Warning: pseudoinverse used at -0.045 #> Warning: neighborhood radius 1.045 #> Warning: reciprocal condition number  1.0154e-30 #> Warning: There are other near singularities as well. 1 #> Warning: pseudoinverse used at -0.045 #> Warning: neighborhood radius 1.045 #> Warning: reciprocal condition number  1.0154e-30 #> Warning: There are other near singularities as well. 1"},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_likert.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot likert scales as centered stacked bars — plot_likert","title":"Plot likert scales as centered stacked bars — plot_likert","text":"Plot likert scales centered stacked bars.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_likert.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot likert scales as centered stacked bars — plot_likert","text":"","code":"plot_likert(   items,   groups = NULL,   groups.titles = \"auto\",   title = NULL,   legend.title = NULL,   legend.labels = NULL,   axis.titles = NULL,   axis.labels = NULL,   catcount = NULL,   cat.neutral = NULL,   sort.frq = NULL,   weight.by = NULL,   title.wtd.suffix = NULL,   wrap.title = 50,   wrap.labels = 30,   wrap.legend.title = 30,   wrap.legend.labels = 28,   geom.size = 0.6,   geom.colors = \"BrBG\",   cat.neutral.color = \"grey70\",   intercept.line.color = \"grey50\",   reverse.colors = FALSE,   values = \"show\",   show.n = TRUE,   show.legend = TRUE,   show.prc.sign = FALSE,   grid.range = 1,   grid.breaks = 0.2,   expand.grid = TRUE,   digits = 1,   reverse.scale = FALSE,   coord.flip = TRUE,   sort.groups = TRUE,   legend.pos = \"bottom\",   rel_heights = 1,   group.legend.options = list(nrow = NULL, byrow = TRUE),   cowplot.options = list(label_x = 0.01, hjust = 0, align = \"v\") )"},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_likert.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot likert scales as centered stacked bars — plot_likert","text":"items Data frame, grouped data frame, column representing one item. groups (optional) Must vector length ncol(items), item vector represents group number related columns items. See 'Examples'. groups.titles (optional, used groups supplied) Titles factor group used table caption component-table. Must character vector length length(unique(groups)). Default \"auto\", means table standard caption Component x. Use NULL use names supplied groups use FALSE suppress table captions. title character vector, used plot title. Depending plot type function, set automatically. title = \"\", title printed. effect-plots, may also character vector length > 1, define titles sub-plot facet. legend.title character vector, used title plot legend. legend.labels character vector labels guide/legend. axis.titles character vector length one two, defining title(s) x-axis y-axis. axis.labels character vector labels used axis labels. Optional argument, since cases, axis labels set automatically. catcount optional, amount categories items (e.g. \"strongly disagree\", \"disagree\", \"agree\" \"strongly agree\" catcount = 4). Note argument applies \"valid\" answers, .e. additional neutral category (see cat.neutral) like \"know\", count catcount (e.g. \"strongly disagree\", \"disagree\", \"agree\", \"strongly agree\" neutral category \"know\" still mean catcount = 4). See 'Note'. cat.neutral neutral category (like \"know\" etc.), specify index number (value) category. Else, set cat.neutral = NULL (default). proportions neutral category answers plotted grey bars left side figure. sort.frq Indicates whether items items ordered total sum positive negative answers. \"pos.asc\" order ascending sum positive answers \"pos.desc\" order descending sum positive answers \"neg.asc\" sorting ascending negative answers \"neg.desc\" sorting descending negative answers NULL (default) sorting weight.Vector weights applied weight cases. Must vector length input vector. Default NULL, weights used. title.wtd.suffix Suffix (string) title, weight.specified, e.g. title.wtd.suffix=\" (weighted)\". Default NULL, title suffix cases weighted. wrap.title numeric, determines many chars plot title displayed one line line break inserted. wrap.labels numeric, determines many chars value, variable axis labels displayed one line line break inserted. wrap.legend.title numeric, determines many chars legend's title displayed one line line break inserted. wrap.legend.labels numeric, determines many chars legend labels displayed one line line break inserted. geom.size size resp. width geoms (bar width, line thickness point size, depending plot type function). Note bar bin widths mostly need smaller values dot sizes. geom.colors user defined color geoms. See 'Details' plot_grpfrq. cat.neutral.color Color neutral category, plotted (see cat.neutral). intercept.line.color Color vertical intercept line divides positive negative values. reverse.colors logical, TRUE, color scale geom.colors reversed, positive negative values switch colors. values Determines style position percentage value labels bars: \"show\" (default) shows percentage value labels middle category bar \"hide\" hides value labels, percentage values bars printed \"sum.inside\" shows sums percentage values negative positive values prints inside end bar \"sum.outside\" shows sums percentage values negative positive values prints outside end bar show.n logical, TRUE, adds total number cases group category labels. show.legend logical, TRUE, depending plot type function, legend added plot. show.prc.sign logical, TRUE, %-signs value labels shown. grid.range Numeric, limits x-axis-range, proportion 100. Default 1, x-scale ranges zero 100% sides center. Can alternatively supplied vector 2 positive numbers (e.g. grid.range = c(1, .8)) set left right limit separately. can use values beyond 1 (100%) case bar labels printed exceed axis range. E.g. grid.range = 1.4 set axis -140 +140%, however, (valid) axis labels -100 +100% printed. Neutral categories adjusted left limit. grid.breaks numeric; sets distance breaks axis, .e. every grid.breaks'th position major grid printed. expand.grid logical, TRUE, plot grid expanded, .e. small margin axes plotting region. Default FALSE. digits Numeric, amount digits decimal point rounding estimates values. reverse.scale logical, TRUE, ordering categories reversed, positive negative values switch position. coord.flip logical, TRUE, x y axis swapped. sort.groups (optional, used groups supplied) logical, groups sorted according values supplied groups. Defaults TRUE. legend.pos (optional, used groups supplied) Defines legend position. Possible values c(\"bottom\", \"top\", \"\", \"\", \"none\"). one group option set \"\" legends printed defined set_theme. rel_heights (optional, used groups supplied) option can used adjust height subplots. bars subplots can different heights due differing number items due legend placement. can adjusted . Takes vector numbers, one plot. Values evaluated relative . group.legend.options (optional, used groups supplied) List options passed guide_legend. notable options byrow=T (default), order categories row wise. group.legend.options = list(nrow = 1) categories can forced single row. cowplot.options (optional, used groups supplied) List label options passed plot_grid.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_likert.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot likert scales as centered stacked bars — plot_likert","text":"ggplot-object.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_likert.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Plot likert scales as centered stacked bars — plot_likert","text":"Note even numbers categories possible plot, \"positive\"        \"negative\" values can splitted two halfs. neutral category (like \"know\")        can used, must indicated cat.neutral.         catcount-argument indicates many item categories        Likert scale. Normally, argument can ignored amount        valid categories retrieved automatically. However, sometimes (instance,        certain category missing items), auto-detection amount        categories fails. cases, specify amount categories        catcount-argument.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_likert.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot likert scales as centered stacked bars — plot_likert","text":"","code":"if (requireNamespace(\"ggrepel\") && requireNamespace(\"sjmisc\")) { library(sjmisc) data(efc) # find all variables from COPE-Index, which all have a \"cop\" in their # variable name, and then plot that subset as likert-plot mydf <- find_var(efc, pattern = \"cop\", out = \"df\")  plot_likert(mydf)  plot_likert(   mydf,   grid.range = c(1.2, 1.4),   expand.grid = FALSE,   values = \"sum.outside\",   show.prc.sign = TRUE )  # Plot in groups  plot_likert(mydf, c(2,1,1,1,1,2,2,2,1))  if (require(\"parameters\") && require(\"nFactors\")) {   groups <- parameters::principal_components(mydf)   plot_likert(mydf, groups = parameters::closest_component(groups)) }  plot_likert(mydf,             c(rep(\"B\", 4), rep(\"A\", 5)),             sort.groups = FALSE,             grid.range = c(0.9, 1.1),             geom.colors = \"RdBu\",             rel_heights = c(6, 8),             wrap.labels = 40,             reverse.scale = TRUE)  # control legend items six_cat_example = data.frame(   matrix(sample(1:6, 600, replace = TRUE),   ncol = 6) )  if (FALSE) { # \\dontrun{ six_cat_example <-   six_cat_example %>%   dplyr::mutate_all(~ordered(.,labels = c(\"+++\",\"++\",\"+\",\"-\",\"--\",\"---\")))  # Old default plot_likert(   six_cat_example,   groups = c(1, 1, 1, 2, 2, 2),   group.legend.options = list(nrow = 2, byrow = FALSE) )  # New default plot_likert(six_cat_example, groups = c(1, 1, 1, 2, 2, 2))  # Single row plot_likert(   six_cat_example,   groups = c(1, 1, 1, 2, 2, 2),   group.legend.options = list(nrow = 1) )} # } } #> Loading required namespace: ggrepel #> Loading required package: parameters #> Loading required package: nFactors #> Loading required package: lattice #>  #> Attaching package: ‘nFactors’ #> The following object is masked from ‘package:lattice’: #>  #>     parallel"},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_model.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot regression models — plot_model","title":"Plot regression models — plot_model","text":"plot_model() creates plots regression models, either   estimates (-called forest dot whisker plots) marginal effects.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot regression models — plot_model","text":"","code":"plot_model(   model,   type = c(\"est\", \"re\", \"eff\", \"emm\", \"pred\", \"int\", \"std\", \"std2\", \"slope\", \"resid\",     \"diag\"),   transform,   terms = NULL,   sort.est = NULL,   rm.terms = NULL,   group.terms = NULL,   order.terms = NULL,   pred.type = c(\"fe\", \"re\"),   mdrt.values = c(\"minmax\", \"meansd\", \"zeromax\", \"quart\", \"all\"),   ri.nr = NULL,   title = NULL,   axis.title = NULL,   axis.labels = NULL,   legend.title = NULL,   wrap.title = 50,   wrap.labels = 25,   axis.lim = NULL,   grid.breaks = NULL,   ci.lvl = NULL,   se = NULL,   robust = FALSE,   vcov.fun = NULL,   vcov.type = NULL,   vcov.args = NULL,   colors = \"Set1\",   show.intercept = FALSE,   show.values = FALSE,   show.p = TRUE,   show.data = FALSE,   show.legend = TRUE,   show.zeroinf = TRUE,   value.offset = NULL,   value.size,   jitter = NULL,   digits = 2,   dot.size = NULL,   line.size = NULL,   vline.color = NULL,   p.threshold = c(0.05, 0.01, 0.001),   p.val = NULL,   p.adjust = NULL,   grid,   case,   auto.label = TRUE,   prefix.labels = c(\"none\", \"varname\", \"label\"),   bpe = \"median\",   bpe.style = \"line\",   bpe.color = \"white\",   ci.style = c(\"whisker\", \"bar\"),   std.response = TRUE,   ... )  get_model_data(   model,   type = c(\"est\", \"re\", \"eff\", \"pred\", \"int\", \"std\", \"std2\", \"slope\", \"resid\", \"diag\"),   transform,   terms = NULL,   sort.est = NULL,   rm.terms = NULL,   group.terms = NULL,   order.terms = NULL,   pred.type = c(\"fe\", \"re\"),   ri.nr = NULL,   ci.lvl = NULL,   colors = \"Set1\",   grid,   case = \"parsed\",   digits = 2,   ... )"},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot regression models — plot_model","text":"model regression model object. Depending type, many kinds models supported, e.g. packages like stats, lme4, nlme, rstanarm, survey, glmmTMB, MASS, brms etc. type Type plot. three groups plot-types: Coefficients (related vignette) type = \"est\" Forest-plot estimates. fitted model   contains one predictor, slope-line plotted. type = \"re\" mixed effects models, plots random   effects. type = \"std\" Forest-plot standardized coefficients. type = \"std2\" Forest-plot standardized coefficients,   however, standardization done dividing two SD (see 'Details'). Marginal Effects  (related vignette) type = \"pred\" Predicted values (marginal effects)   specific model terms. See ggpredict details. type = \"eff\" Similar type = \"pred\", however,   discrete predictors held constant proportions (reference   level). See ggeffect details. type = \"emm\" Similar type = \"eff\", see   ggemmeans details. type = \"int\" Marginal effects interaction terms   model. Model diagnostics type = \"slope\" Slope coefficients single   predictor, response (linear relationship model   term response). See 'Details'. type = \"resid\" Slope coefficients single   predictor, residuals (linear relationship model   term residuals). See 'Details'. type = \"diag\" Check model assumptions. See 'Details'. Note: mixed models, diagnostic plots like linear relationship check Homoscedasticity, take uncertainty random effects account, based fixed effects part model. transform character vector, naming function applied estimates confidence intervals. default, transform automatically use \"exp\" transformation applicable classes model (e.g. logistic poisson regression). Estimates linear models remain untransformed. Use NULL want raw, non-transformed estimates. terms Character vector names terms model  plotted. argument depends plot-type: Coefficients Select terms plotted.      term removed output. Note term names must match      names model's coefficients. factors, means      variable name suffixed related factor level,      category counts one term. E.g. rm.terms = \"t_name [2,3]\"      remove terms \"t_name2\" \"t_name3\" (assuming      variable t_name categorical least      factor levels 2 3). Another example      iris-dataset: terms = \"Species\" work, instead      write terms = \"Species [versicolor,virginica]\"      remove two levels, terms = \"Speciesversicolor\"      just want remove level versicolor plot. Marginal Effects terms indicates      terms marginal effects displayed. least one term      required calculate effects, maximum length three terms,      second third term indicate groups, .e. predictions      first term grouped levels second (third) term.      terms may also indicate higher order terms (e.g. interaction      terms). Indicating levels square brackets allows selecting      specific groups. Term name levels brackets must separated      whitespace character, e.g. terms = c(\"age\", \"education [1,3]\").      also possible specify range numeric values      predictions colon, instance terms = c(\"education [1,3]\",      \"age [30:50]\"). Furthermore, possible specify function name.      Values predictions transformed, e.g.      terms = \"income [exp]\". useful model predictors      transformed fitting model back-transformed      original scale predictions. Finally, numeric vectors      specific values given, \"pretty range\" calculated, avoid      memory allocation problems vectors many unique values.      numeric vector specified second third term (.e. vector      represents grouping structure), representative values (see      values_at) chosen. values      numeric vector used compute predictions, may use      e.g. terms = \"age []\". details, see      ggpredict. sort.est Determines way estimates sorted plot: NULL (default), sorting done estimates sorted order appear model formula. TRUE, estimates sorted descending order, highest estimate top. sort.est = \"sort.\", estimates re-sorted coefficient (applies type = \"re\" grid = FALSE), .e. estimates random effects predictor sorted plotted plot. type = \"re\", specify predictor's / coefficient's name sort estimates according random effect. rm.terms Character vector names indicate terms removed plot. Counterpart terms. rm.terms = \"t_name\" remove term t_name. Default NULL, .e. terms used. factors, levels removed plot need explicitely indicated square brackets, match model's coefficient names, e.g. rm.terms = \"t_name [2,3]\" remove terms \"t_name2\" \"t_name3\" (assuming variable t_name categorical least factor levels 2 3). Another example iris dataset rm.terms = \"Species [versicolor,virginica]\". Note rm.terms-argument apply Marginal Effects plots. group.terms Numeric vector group indices, group coefficients. group coefficients gets color (see 'Examples'). order.terms Numeric vector, indicating order coefficients plotted. See examples package-vignette. pred.type Character, applies Marginal Effects plots mixed effects models. Indicates whether predicted values conditioned random effects (pred.type = \"re\") fixed effects (pred.type = \"fe\", default). details, see documentation type-argument ggpredict. mdrt.values Indicates values moderator variable used plotting interaction terms (.e. type = \"int\"). \"minmax\" (default) minimum maximum values (lower upper bounds) moderator used plot interaction independent variable moderator(s). \"meansd\" uses mean value moderator well one standard deviation mean value plot effect moderator independent variable (following convention suggested Cohen Cohen popularized Aiken West (1991), .e. using mean, value one standard deviation , value one standard deviation mean values moderator, see Grace-Martin K: 3 Tips Make Interpreting Moderation Effects Easier). \"zeromax\" similar \"minmax\" option, however, 0 always used minimum value moderator. may useful predictors empirical zero-value, absence moderation simulated using 0 minimum. \"quart\" calculates uses quartiles (lower, median upper) moderator value. \"\" uses values moderator variable. ri.nr Numeric vector. type = \"re\" fitted model one random intercept, ri.nr indicates random effects random intercept (: list elements ranef) plotted. Default NULL, random effects plotted. title Character vector, used plot title. default, response_labels called retrieve label dependent variable, used title. Use title = \"\" remove title. axis.title Character vector length one two (depending plot function type), used title(s) x y axis. specified, default labelling  chosen. Note: plot types may support argument sufficiently. cases, use returned ggplot-object add axis titles manually labs. Use axis.title = \"\" remove axis titles. axis.labels Character vector labels model terms, used axis labels. default, term_labels called retrieve labels coefficients, used axis labels. Use axis.labels = \"\" auto.label = FALSE use variable names labels instead. axis.labels named vector, axis labels (default, names model's coefficients) matched names axis.label. ensures labels always match related axis value, matter way axis labels sorted. legend.title Character vector, used legend title plots legend. wrap.title Numeric, determines many chars plot title displayed one line line break inserted. wrap.labels Numeric, determines many chars value, variable axis labels displayed one line line break inserted. axis.lim Numeric vector length 2, defining range plot axis. Depending plot-type, may effect either x- y-axis. Marginal Effects plots, axis.lim may also list two vectors length 2, defining axis limits x y axis. grid.breaks Numeric value vector; grid.breaks single value, sets distance breaks axis every grid.breaks'th position, major grid line plotted. grid.breaks vector, values used define axis positions major grid lines. ci.lvl Numeric, level confidence intervals (error bars). Use ci.lvl = NA remove error bars. stanreg-models, ci.lvl defines (outer) probability credible interval plotted (see ci). default, stanreg-models printed two intervals: \"inner\" interval, defaults 50%-CI; \"outer\" interval, defaults 89%-CI. ci.lvl affects outer interval cases. See prob.inner prob.outer ...-argument details. se Logical, TRUE, standard errors also printed. robust standard errors required, use arguments vcov.fun, vcov.type vcov.args (see standard_error details), use argument robust shortcut. se overrides ci.lvl: NULL, arguments ci.lvl transform ignored. Currently, se applies Coefficients plots. robust Deprecated. Please use vcov.fun directly specify estimation variance-covariance matrix. vcov.fun Variance-covariance matrix used compute uncertainty estimates (e.g., robust standard errors). argument accepts covariance matrix, function returns covariance matrix, string identifies function used compute covariance matrix. See model_parameters(). vcov.type Deprecated. type-argument now included vcov.args. vcov.args List arguments passed function identified vcov.fun argument. function typically supplied sandwich clubSandwich packages. Please refer documentation (e.g., ?sandwich::vcovHAC) see list available arguments. colors May character vector color values hex-format, valid color value names (see demo(\"colors\")) name pre-defined color palette. Following options valid colors argument: specified, default color brewer palette used, suitable plot style. \"gs\", greyscale used. \"bw\", plot-type line-plot, plot black/white uses different line types distinguish groups (see package-vignette). colors valid color brewer palette name, related palette used. Use RColorBrewer::display.brewer.() view available palette names. pre-defined color palettes package, see sjPlot-themes details. Else specify color values names vector (e.g. colors = \"#00ff00\" colors = c(\"firebrick\", \"blue\")). show.intercept Logical, TRUE, intercept fitted model also plotted. Default FALSE. transform = \"exp\", please note due exponential transformation estimates, intercept cases non-finite plot can created. show.values Logical, whether values plotted . show.p Logical, adds asterisks indicate significance level estimates value labels. show.data Logical, Marginal Effects plots, also plots raw data points. show.legend Marginal Effects plots, shows hides legend. show.zeroinf Logical, TRUE, shows zero-inflation part hurdle- zero-inflated models. value.offset Numeric, offset text labels adjust position relative dots lines. value.size Numeric, indicates size value labels. Can used plot types argument show.values applicable, e.g. value.size = 4. jitter Numeric, 0 1. show.data = TRUE, can add small amount random variation location data point. jitter indicates width, .e. much bin's width occupied jittered values. digits Numeric, amount digits decimal point rounding estimates values. dot.size Numeric, size dots indicate point estimates. line.size Numeric, size lines indicate error bars. vline.color Color vertical \"zero effect\" line. Default color inherited current theme. p.threshold Numeric vector length 3, indicating treshold annotating p-values asterisks. applies p.style = \"asterisk\". p.val Character specifying method used calculate p-values. Defaults \"profile\" glm/polr models, otherwise \"wald\". p.adjust Character vector, NULL, indicates method adjust p-values. See p.adjust details. grid Logical, TRUE, multiple plots plotted grid layout. case Desired target case. Labels automatically converted specified character case. See snakecase::to_any_case() details argument. default, case specified, set \"parsed\", unless prefix.labels \"none\". prefix.labels either \"label\" (\"l\") \"varname\" (\"v\") case specified, set NULL - convenient default prefixing labels. auto.label Logical, TRUE (default),  data labelled,  term_labels called retrieve labels  coefficients, used predictor labels. data  labelled, format_parameters()  used create pretty labels. auto.label = FALSE, original variable names value labels (factor levels) used. prefix.labels Indicates whether value labels categorical variables prefixed, e.g. variable name variable label. See argument prefix term_labels details. bpe Stan-models (fitted rstanarm- brms-package), Bayesian point estimate , default, median posterior distribution. Use bpe define functions calculate Bayesian point estimate. bpe needs character naming specific function, passed fun-argument typical_value. , bpe = \"mean\" calculate mean value posterior distribution. bpe.style Stan-models (fitted rstanarm- brms-package), Bayesian point estimate indicated small, vertical line default. Use bpe.style = \"dot\" plot dot instead line point estimate. bpe.color Character vector, indicating color Bayesian point estimate. Setting bpe.color = NULL inherit color mapped aesthetic match geom's color. ci.style Character vector, defining whether inner outer intervals Bayesion models shown boxplot-style (\"whisker\") bars different alpha-levels (\"bar\"). std.response Logical, whether response variable also standardized standardized coefficients requested. Setting std.response = TRUE show.std = TRUE behave complete data standardized fitting model. ... arguments, passed various functions. list supported arguments description detail. prob.inner prob.outer Stan-models     (fitted rstanarm- brms-package) coefficients     plot-types, can specify numeric values 0 1     prob.inner prob.outer, used inner     outer probabilities uncertainty intervals (HDI). default,     inner probability 0.5 outer probability 0.89 (unless     ci.lvl specified - case, ci.lvl used outer     probability). size.inner Stan-models Coefficients     plot-types, can specify width bar inner     probabilities. Default 0.1. Setting size.inner = 0     removes inner probability regions. width, alpha, scale Passed     geom_errorbar() geom_density_ridges(),     forest diagnostic plots. width, alpha, dot.alpha, dodge log.y Passed     plot.ggeffects Marginal Effects     plots. show.loess Logical, diagnostic plot-types \"slope\"     \"resid\", adds (hides) loess-smoothed line plot. Marginal Effects plot-types plotting marginal effects,     arguments also passed ggpredict,     ggeffect plot.ggeffects. Case conversion labels case conversion labels (see argument     case), arguments sep_in sep_out passed     snakecase::to_any_case().     applies automatically retrieved term labels,     term labels provided axis.labels-argument.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot regression models — plot_model","text":"Depending plot-type, plot_model() returns   ggplot-object list objects. get_model_data   returns associated data plot-object tidy data frame,   (depending plot-type) list data frames.","code":""},{"path":[]},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_model.html","id":"different-plot-types","dir":"Reference","previous_headings":"","what":"Different Plot Types","title":"Plot regression models — plot_model","text":"type = \"std\" Plots standardized estimates. See details . type = \"std2\" Plots standardized estimates, however,     standardization follows Gelman's (2008) suggestion, rescaling     estimates dividing two standard deviations instead just one.     Resulting coefficients directly comparable untransformed     binary predictors. type = \"pred\" Plots estimated marginal means (marginal effects).     Simply wraps ggpredict. See also     package-vignette. type = \"eff\" Plots estimated marginal means (marginal effects).     Simply wraps ggeffect. See also     package-vignette. type = \"int\" shortcut marginal effects plots,     interaction terms automatically detected used     terms-argument. Furthermore, moderator variable (second     - third - term interaction) continuous, type = \"int\"     automatically chooses useful values based mdrt.values-argument,     passed terms. , ggpredict     called. type = \"int\" plots interaction term appears     first formula along x-axis, second (possibly     third) variable interaction used grouping factor(s)     (moderating variable). Use type = \"pred\" type = \"eff\"     specify certain order terms-argument indicate     variable(s) used moderator. See also     package-vignette. type = \"slope\" type = \"resid\" Simple diagnostic-plots,   linear model single predictor plotted   response variable, model's residuals. Additionally, loess-smoothed   line added plot. main purpose plots check whether   relationship outcome (residuals) predictor roughly   linear . Since plots based simple linear regression   one model predictor moment, slopes (.e. coefficients) may   differ coefficients complete model. type = \"diag\" Stan-models, plots prior versus   posterior samples. linear (mixed) models, plots   multicollinearity-check (Variance Inflation Factors), QQ-plots,   checks normal distribution residuals homoscedasticity   (constant variance residuals) shown. generalized   linear mixed models, returns QQ-plot random effects.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_model.html","id":"standardized-estimates","dir":"Reference","previous_headings":"","what":"Standardized Estimates","title":"Plot regression models — plot_model","text":"Default standardization done completely refitting model   standardized data. Hence, approach equal standardizing   variables fitting model, particularly recommended   complex models include interactions transformations (e.g., polynomial   spline terms). type = \"std2\", standardization estimates   follows Gelman's (2008)   suggestion, rescaling estimates dividing two standard deviations   instead just one. Resulting coefficients directly comparable   untransformed binary predictors.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_model.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Plot regression models — plot_model","text":"Gelman (2008) \"Scaling regression inputs dividing two   standard deviations.\" Statistics Medicine 27: 2865-2873.   http://www.stat.columbia.edu/~gelman/research/published/standardizing7.pdf      Aiken West (1991). Multiple Regression: Testing Interpreting Interactions.","code":""},{"path":[]},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_models.html","id":null,"dir":"Reference","previous_headings":"","what":"Forest plot of multiple regression models — plot_models","title":"Forest plot of multiple regression models — plot_models","text":"Plot compare regression coefficients confidence   intervals multiple regression models one plot.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_models.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Forest plot of multiple regression models — plot_models","text":"","code":"plot_models(   ...,   transform = NULL,   std.est = NULL,   std.response = TRUE,   rm.terms = NULL,   title = NULL,   m.labels = NULL,   legend.title = \"Dependent Variables\",   legend.pval.title = \"p-level\",   axis.labels = NULL,   axis.title = NULL,   axis.lim = NULL,   wrap.title = 50,   wrap.labels = 25,   wrap.legend.title = 20,   grid.breaks = NULL,   dot.size = 3,   line.size = NULL,   value.size = NULL,   spacing = 0.4,   colors = \"Set1\",   show.values = FALSE,   show.legend = TRUE,   show.intercept = FALSE,   show.p = TRUE,   p.shape = FALSE,   p.threshold = c(0.05, 0.01, 0.001),   p.adjust = NULL,   ci.lvl = 0.95,   robust = FALSE,   vcov.fun = NULL,   vcov.type = c(\"HC3\", \"const\", \"HC\", \"HC0\", \"HC1\", \"HC2\", \"HC4\", \"HC4m\", \"HC5\"),   vcov.args = NULL,   vline.color = NULL,   digits = 2,   grid = FALSE,   auto.label = TRUE,   prefix.labels = c(\"none\", \"varname\", \"label\") )"},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_models.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Forest plot of multiple regression models — plot_models","text":"... One regression models, including glm's mixed models. May also list fitted models. See 'Examples'. transform character vector, naming function applied estimates confidence intervals. default, transform automatically use \"exp\" transformation applicable classes model (e.g. logistic poisson regression). Estimates linear models remain untransformed. Use NULL want raw, non-transformed estimates. std.est Choose whether standardized coefficients used plotting. Default standardization (std.est = NULL). May \"std\" standardized beta values \"std2\", standardization done rescaling estimates dividing two sd. std.response Logical, whether response variable also standardized standardized coefficients requested. Setting std.response = TRUE show.std = TRUE behave complete data standardized fitting model. rm.terms Character vector names indicate terms removed plot. Counterpart terms. rm.terms = \"t_name\" remove term t_name. Default NULL, .e. terms used. factors, levels removed plot need explicitely indicated square brackets, match model's coefficient names, e.g. rm.terms = \"t_name [2,3]\" remove terms \"t_name2\" \"t_name3\" (assuming variable t_name categorical least factor levels 2 3). Another example iris dataset rm.terms = \"Species [versicolor,virginica]\". Note rm.terms-argument apply Marginal Effects plots. title Character vector, used plot title. default, response_labels called retrieve label dependent variable, used title. Use title = \"\" remove title. m.labels Character vector, used indicate different models plot's legend. specified, labels dependent variables model used. legend.title Character vector, used legend title plots legend. legend.pval.title Character vector, used title plot legend indicates p-values. Default \"p-level\". applies p.shape = TRUE. axis.labels Character vector labels model terms, used axis labels. default, term_labels called retrieve labels coefficients, used axis labels. Use axis.labels = \"\" auto.label = FALSE use variable names labels instead. axis.labels named vector, axis labels (default, names model's coefficients) matched names axis.label. ensures labels always match related axis value, matter way axis labels sorted. axis.title Character vector length one two (depending plot function type), used title(s) x y axis. specified, default labelling  chosen. Note: plot types may support argument sufficiently. cases, use returned ggplot-object add axis titles manually labs. Use axis.title = \"\" remove axis titles. axis.lim Numeric vector length 2, defining range plot axis. Depending plot-type, may effect either x- y-axis. Marginal Effects plots, axis.lim may also list two vectors length 2, defining axis limits x y axis. wrap.title Numeric, determines many chars plot title displayed one line line break inserted. wrap.labels Numeric, determines many chars value, variable axis labels displayed one line line break inserted. wrap.legend.title numeric, determines many chars legend's title displayed one line line break inserted. grid.breaks Numeric value vector; grid.breaks single value, sets distance breaks axis every grid.breaks'th position, major grid line plotted. grid.breaks vector, values used define axis positions major grid lines. dot.size Numeric, size dots indicate point estimates. line.size Numeric, size lines indicate error bars. value.size Numeric, indicates size value labels. Can used plot types argument show.values applicable, e.g. value.size = 4. spacing Numeric, spacing dots error bars plotted fitted models. Default 0.3. colors May character vector color values hex-format, valid color value names (see demo(\"colors\")) name pre-defined color palette. Following options valid colors argument: specified, default color brewer palette used, suitable plot style. \"gs\", greyscale used. \"bw\", plot-type line-plot, plot black/white uses different line types distinguish groups (see package-vignette). colors valid color brewer palette name, related palette used. Use RColorBrewer::display.brewer.() view available palette names. pre-defined color palettes package, see sjPlot-themes details. Else specify color values names vector (e.g. colors = \"#00ff00\" colors = c(\"firebrick\", \"blue\")). show.values Logical, whether values plotted . show.legend Marginal Effects plots, shows hides legend. show.intercept Logical, TRUE, intercept fitted model also plotted. Default FALSE. transform = \"exp\", please note due exponential transformation estimates, intercept cases non-finite plot can created. show.p Logical, adds asterisks indicate significance level estimates value labels. p.shape Logical, TRUE, significant levels distinguished different point shapes related legend plotted. Default FALSE. p.threshold Numeric vector length 3, indicating treshold annotating p-values asterisks. applies p.style = \"asterisk\". p.adjust Character vector, NULL, indicates method adjust p-values. See p.adjust details. ci.lvl Numeric, level confidence intervals (error bars). Use ci.lvl = NA remove error bars. stanreg-models, ci.lvl defines (outer) probability credible interval plotted (see ci). default, stanreg-models printed two intervals: \"inner\" interval, defaults 50%-CI; \"outer\" interval, defaults 89%-CI. ci.lvl affects outer interval cases. See prob.inner prob.outer ...-argument details. robust Deprecated. Please use vcov.fun directly specify estimation variance-covariance matrix. vcov.fun Variance-covariance matrix used compute uncertainty estimates (e.g., robust standard errors). argument accepts covariance matrix, function returns covariance matrix, string identifies function used compute covariance matrix. See model_parameters(). vcov.type Deprecated. type-argument now included vcov.args. vcov.args List arguments passed function identified vcov.fun argument. function typically supplied sandwich clubSandwich packages. Please refer documentation (e.g., ?sandwich::vcovHAC) see list available arguments. vline.color Color vertical \"zero effect\" line. Default color inherited current theme. digits Numeric, amount digits decimal point rounding estimates values. grid Logical, TRUE, multiple plots plotted grid layout. auto.label Logical, TRUE (default),  data labelled,  term_labels called retrieve labels  coefficients, used predictor labels. data  labelled, format_parameters()  used create pretty labels. auto.label = FALSE, original variable names value labels (factor levels) used. prefix.labels Indicates whether value labels categorical variables prefixed, e.g. variable name variable label. See argument prefix term_labels details.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_models.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Forest plot of multiple regression models — plot_models","text":"ggplot-object.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_models.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Forest plot of multiple regression models — plot_models","text":"","code":"data(efc)  # fit three models fit1 <- lm(barthtot ~ c160age + c12hour + c161sex + c172code, data = efc) fit2 <- lm(neg_c_7 ~ c160age + c12hour + c161sex + c172code, data = efc) fit3 <- lm(tot_sc_e ~ c160age + c12hour + c161sex + c172code, data = efc)  # plot multiple models plot_models(fit1, fit2, fit3, grid = TRUE)   # plot multiple models with legend labels and # point shapes instead of value labels plot_models(   fit1, fit2, fit3,   axis.labels = c(     \"Carer's Age\", \"Hours of Care\", \"Carer's Sex\", \"Educational Status\"   ),   m.labels = c(\"Barthel Index\", \"Negative Impact\", \"Services used\"),   show.values = FALSE, show.p = FALSE, p.shape = TRUE )   if (FALSE) { # \\dontrun{ # plot multiple models from nested lists argument all.models <- list() all.models[[1]] <- fit1 all.models[[2]] <- fit2 all.models[[3]] <- fit3  plot_models(all.models)  # plot multiple models with different predictors (stepwise inclusion), # standardized estimates fit1 <- lm(mpg ~ wt + cyl + disp + gear, data = mtcars) fit2 <- update(fit1, . ~ . + hp) fit3 <- update(fit2, . ~ . + am)  plot_models(fit1, fit2, fit3, std.est = \"std2\") } # }"},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_residuals.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot predicted values and their residuals — plot_residuals","title":"Plot predicted values and their residuals — plot_residuals","text":"function plots observed predicted values response              linear (mixed) models coefficient highlights              observed values according distance (residuals)              predicted values. allows investigate well actual              predicted values outcome fit across predictor variables.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_residuals.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot predicted values and their residuals — plot_residuals","text":"","code":"plot_residuals(   fit,   geom.size = 2,   remove.estimates = NULL,   show.lines = TRUE,   show.resid = TRUE,   show.pred = TRUE,   show.ci = FALSE )"},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_residuals.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot predicted values and their residuals — plot_residuals","text":"fit Fitted linear (mixed) regression model (including objects class gls plm). geom.size size resp. width geoms (bar width, line thickness point size, depending plot type function). Note bar bin widths mostly need smaller values dot sizes. remove.estimates Numeric vector indices (order equals row index coef(fit)) character vector coefficient names indicate estimates removed table output. first estimate intercept, followed model predictors. intercept removed table output! remove.estimates = c(2:4) remove 2nd 4th estimate (1st 3rd predictor intercept) output. remove.estimates = \"est_name\" remove estimate est_name. Default NULL, .e. estimates printed. show.lines Logical, TRUE, line connecting predicted residual values plotted. Set argument FALSE, plot-building time consuming. show.resid Logical, TRUE, residual values plotted. show.pred Logical, TRUE, predicted values plotted. show.ci Logical, TRUE), adds notches box plot, used compare groups; notches two boxes overlap, medians considered significantly different.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_residuals.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot predicted values and their residuals — plot_residuals","text":"ggplot-object.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_residuals.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Plot predicted values and their residuals — plot_residuals","text":"actual (observed) values coloured fill, predicted       values solid outline without filling.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_residuals.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot predicted values and their residuals — plot_residuals","text":"","code":"data(efc) # fit model fit <- lm(neg_c_7 ~ c12hour + e17age + e42dep, data = efc)  # plot residuals for all independent variables plot_residuals(fit) #> `geom_smooth()` using formula = 'y ~ x'   # remove some independent variables from output plot_residuals(fit, remove.estimates = c(\"e17age\", \"e42dep\")) #> `geom_smooth()` using formula = 'y ~ x'"},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_scatter.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot (grouped) scatter plots — plot_scatter","title":"Plot (grouped) scatter plots — plot_scatter","text":"Display scatter plot two variables. Adding grouping variable   scatter plot possible. Furthermore, fitted lines can added   group well overall plot.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_scatter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot (grouped) scatter plots — plot_scatter","text":"","code":"plot_scatter(   data,   x,   y,   grp,   title = \"\",   legend.title = NULL,   legend.labels = NULL,   dot.labels = NULL,   axis.titles = NULL,   dot.size = 1.5,   label.size = 3,   colors = \"metro\",   fit.line = NULL,   fit.grps = NULL,   show.rug = FALSE,   show.legend = TRUE,   show.ci = FALSE,   wrap.title = 50,   wrap.legend.title = 20,   wrap.legend.labels = 20,   jitter = 0.05,   emph.dots = FALSE,   grid = FALSE )"},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_scatter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot (grouped) scatter plots — plot_scatter","text":"data data frame, grouped data frame. x Name variable x-axis. y Name variable y-axis. grp Optional, name grouping-variable. missing, scatter plot grouped. See 'Examples'. title Character vector, used plot title. default, response_labels called retrieve label dependent variable, used title. Use title = \"\" remove title. legend.title Character vector, used legend title plots legend. legend.labels character vector labels guide/legend. dot.labels Character vector names coordinate pair given x y, text labels added plot. Must length x y. dot.labels different length, data points trimmed match dot.labels. dot.labels = NULL (default), labels printed. axis.titles character vector length one two, defining title(s) x-axis y-axis. dot.size Numeric, size dots indicate point estimates. label.size Size text labels argument dot.labels used. colors May character vector color values hex-format, valid color value names (see demo(\"colors\")) name pre-defined color palette. Following options valid colors argument: specified, default color brewer palette used, suitable plot style. \"gs\", greyscale used. \"bw\", plot-type line-plot, plot black/white uses different line types distinguish groups (see package-vignette). colors valid color brewer palette name, related palette used. Use RColorBrewer::display.brewer.() view available palette names. pre-defined color palettes package, see sjPlot-themes details. Else specify color values names vector (e.g. colors = \"#00ff00\" colors = c(\"firebrick\", \"blue\")). fit.line, fit.grps Specifies method add fitted line accross data points. Possible values instance \"lm\", \"glm\", \"loess\" \"auto\". NULL, line plotted. fit.line adds fitted line complete data, fit.grps adds fitted line subgroup grp. show.rug Logical, TRUE, marginal rug plot displayed graph. show.legend Marginal Effects plots, shows hides legend. show.ci Logical, TRUE), adds notches box plot, used compare groups; notches two boxes overlap, medians considered significantly different. wrap.title Numeric, determines many chars plot title displayed one line line break inserted. wrap.legend.title numeric, determines many chars legend's title displayed one line line break inserted. wrap.legend.labels numeric, determines many chars legend labels displayed one line line break inserted. jitter Numeric, 0 1. show.data = TRUE, can add small amount random variation location data point. jitter indicates width, .e. much bin's width occupied jittered values. emph.dots Logical, TRUE, overlapping points coordinates becomme larger, point size indicates amount overlapping. grid Logical, TRUE, multiple plots plotted grid layout.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_scatter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot (grouped) scatter plots — plot_scatter","text":"ggplot-object. grouped data frames, list ggplot-objects   group data.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_scatter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot (grouped) scatter plots — plot_scatter","text":"","code":"# load sample date library(sjmisc) library(sjlabelled) data(efc)  # simple scatter plot plot_scatter(efc, e16sex, neg_c_7)   # simple scatter plot, increased jittering plot_scatter(efc, e16sex, neg_c_7, jitter = .4)   # grouped scatter plot plot_scatter(efc, c160age, e17age, e42dep)   # grouped scatter plot with marginal rug plot # and add fitted line for complete data plot_scatter(   efc, c12hour, c160age, c172code,   show.rug = TRUE, fit.line = \"lm\" ) #> `geom_smooth()` using formula = 'y ~ x'   # grouped scatter plot with marginal rug plot # and add fitted line for each group plot_scatter(   efc, c12hour, c160age, c172code,   show.rug = TRUE, fit.grps = \"loess\",   grid = TRUE ) #> `geom_smooth()` using formula = 'y ~ x'"},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_stackfrq.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot stacked proportional bars — plot_stackfrq","title":"Plot stacked proportional bars — plot_stackfrq","text":"Plot items (variables) scale stacked proportional bars.                function useful several items identical scale/categoroies                plotted compare distribution answers.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_stackfrq.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot stacked proportional bars — plot_stackfrq","text":"","code":"plot_stackfrq(   items,   title = NULL,   legend.title = NULL,   legend.labels = NULL,   axis.titles = NULL,   axis.labels = NULL,   weight.by = NULL,   sort.frq = NULL,   wrap.title = 50,   wrap.labels = 30,   wrap.legend.title = 30,   wrap.legend.labels = 28,   geom.size = 0.5,   geom.colors = \"Blues\",   show.prc = TRUE,   show.n = FALSE,   show.total = TRUE,   show.axis.prc = TRUE,   show.legend = TRUE,   grid.breaks = 0.2,   expand.grid = FALSE,   digits = 1,   vjust = \"center\",   coord.flip = TRUE )"},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_stackfrq.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot stacked proportional bars — plot_stackfrq","text":"items Data frame, grouped data frame, column representing one item. title character vector, used plot title. Depending plot type function, set automatically. title = \"\", title printed. effect-plots, may also character vector length > 1, define titles sub-plot facet. legend.title character vector, used title plot legend. legend.labels character vector labels guide/legend. axis.titles character vector length one two, defining title(s) x-axis y-axis. axis.labels character vector labels used axis labels. Optional argument, since cases, axis labels set automatically. weight.Vector weights applied weight cases. Must vector length input vector. Default NULL, weights used. sort.frq Indicates whether items ordered highest count first last category items. \"first.asc\" order ascending lowest count first category, \"first.desc\" order descending lowest count first category, \"last.asc\" order ascending lowest count last category, \"last.desc\" order descending lowest count last category, NULL (default) sorting. wrap.title numeric, determines many chars plot title displayed one line line break inserted. wrap.labels numeric, determines many chars value, variable axis labels displayed one line line break inserted. wrap.legend.title numeric, determines many chars legend's title displayed one line line break inserted. wrap.legend.labels numeric, determines many chars legend labels displayed one line line break inserted. geom.size size resp. width geoms (bar width, line thickness point size, depending plot type function). Note bar bin widths mostly need smaller values dot sizes. geom.colors user defined color geoms. See 'Details' plot_grpfrq. show.prc Logical, whether percentage values plotted . show.n Logical, whether count values hould plotted . show.total logical, TRUE, adds total number cases group category labels. show.axis.prc Logical, TRUE (default), percentage values x-axis shown. show.legend logical, TRUE, depending plot type function, legend added plot. grid.breaks numeric; sets distance breaks axis, .e. every grid.breaks'th position major grid printed. expand.grid logical, TRUE, plot grid expanded, .e. small margin axes plotting region. Default FALSE. digits Numeric, amount digits decimal point rounding estimates values. vjust character vector, indicating vertical position value labels. Allowed values vjust aesthetics ggplot2: \"left\", \"center\", \"right\", \"bottom\", \"middle\", \"top\" new options like \"inward\" \"outward\", align text towards away center plot respectively. coord.flip logical, TRUE, x y axis swapped.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_stackfrq.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot stacked proportional bars — plot_stackfrq","text":"ggplot-object.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_stackfrq.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot stacked proportional bars — plot_stackfrq","text":"","code":"# Data from the EUROFAMCARE sample dataset library(sjmisc) data(efc) # recveive first item of COPE-index scale start <- which(colnames(efc) == \"c82cop1\") # recveive first item of COPE-index scale end <- which(colnames(efc) == \"c90cop9\") # auto-detection of labels plot_stackfrq(efc[, start:end])   # works on grouped data frames as well library(dplyr) efc %>%   group_by(c161sex) %>%   select(start:end) %>%   plot_stackfrq() #> Warning: Using an external vector in selections was deprecated in tidyselect 1.1.0. #> ℹ Please use `all_of()` or `any_of()` instead. #>   # Was: #>   data %>% select(start) #>  #>   # Now: #>   data %>% select(all_of(start)) #>  #> See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>. #> Warning: Using an external vector in selections was deprecated in tidyselect 1.1.0. #> ℹ Please use `all_of()` or `any_of()` instead. #>   # Was: #>   data %>% select(end) #>  #>   # Now: #>   data %>% select(all_of(end)) #>  #> See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>. #> Adding missing grouping variables: `c161sex` #> [[1]] #> [[1]][[1]] #> NULL #>  #> [[1]][[2]]  #>  #>  #> [[2]]  #>"},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_xtab.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot contingency tables — plot_xtab","title":"Plot contingency tables — plot_xtab","text":"Plot proportional crosstables (contingency tables) two variables ggplot diagram.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_xtab.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot contingency tables — plot_xtab","text":"","code":"plot_xtab(   x,   grp,   type = c(\"bar\", \"line\"),   margin = c(\"col\", \"cell\", \"row\"),   bar.pos = c(\"dodge\", \"stack\"),   title = \"\",   title.wtd.suffix = NULL,   axis.titles = NULL,   axis.labels = NULL,   legend.title = NULL,   legend.labels = NULL,   weight.by = NULL,   rev.order = FALSE,   show.values = TRUE,   show.n = TRUE,   show.prc = TRUE,   show.total = TRUE,   show.legend = TRUE,   show.summary = FALSE,   summary.pos = \"r\",   drop.empty = TRUE,   string.total = \"Total\",   wrap.title = 50,   wrap.labels = 15,   wrap.legend.title = 20,   wrap.legend.labels = 20,   geom.size = 0.7,   geom.spacing = 0.1,   geom.colors = \"Paired\",   dot.size = 3,   smooth.lines = FALSE,   grid.breaks = 0.2,   expand.grid = FALSE,   ylim = NULL,   vjust = \"bottom\",   hjust = \"center\",   y.offset = NULL,   coord.flip = FALSE )"},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_xtab.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot contingency tables — plot_xtab","text":"x vector values (variable) describing bars make plot. grp Grouping variable length x, x grouped categories represented grp. type Plot type. may either \"bar\" (default) bar charts, \"line\" line diagram. margin Indicates data proportional table plotted. Use \"row\" calculating row percentages, \"col\" column percentages \"cell\" cell percentages. margin = \"col\", additional bar total sum column can added plot (see show.total). bar.pos Indicates whether bars positioned side--side (default), stacked (bar.pos = \"stack\"). May abbreviated. title character vector, used plot title. Depending plot type function, set automatically. title = \"\", title printed. effect-plots, may also character vector length > 1, define titles sub-plot facet. title.wtd.suffix Suffix (string) title, weight.specified, e.g. title.wtd.suffix=\" (weighted)\". Default NULL, title suffix cases weighted. axis.titles character vector length one two, defining title(s) x-axis y-axis. axis.labels character vector labels used axis labels. Optional argument, since cases, axis labels set automatically. legend.title character vector, used title plot legend. legend.labels character vector labels guide/legend. weight.Vector weights applied weight cases. Must vector length input vector. Default NULL, weights used. rev.order Logical, TRUE, order categories (groups) reversed. show.values Logical, whether values plotted . show.n logical, TRUE, adds total number cases group category labels. show.prc logical, TRUE (default), percentage values plotted bar FALSE, percentage values removed. show.total margin = \"col\", additional bar sum within category percentages added category. show.legend logical, TRUE, depending plot type function, legend added plot. show.summary logical, TRUE (default), summary chi-squared statistics (see chisq.test), Cramer's V Phi-value etc. shown. cell contains expected values lower five (lower 10 df 1), Fisher's exact test (see fisher.test) computed instead chi-squared test. table's matrix larger 2x2, Fisher's exact test Monte Carlo simulation computed. summary.pos position model summary printed show.summary TRUE. Default \"r\", .e. printed upper right corner. Use \"l\" upper left corner. drop.empty Logical, TRUE variable's values labeled, values / factor levels occurrence data omitted output. FALSE, labeled values observations still printed table (frequency 0). string.total String legend label total-column added. applies show.total = TRUE. Default \"Total\". wrap.title numeric, determines many chars plot title displayed one line line break inserted. wrap.labels numeric, determines many chars value, variable axis labels displayed one line line break inserted. wrap.legend.title numeric, determines many chars legend's title displayed one line line break inserted. wrap.legend.labels numeric, determines many chars legend labels displayed one line line break inserted. geom.size size resp. width geoms (bar width, line thickness point size, depending plot type function). Note bar bin widths mostly need smaller values dot sizes. geom.spacing spacing geoms (.e. bar spacing) geom.colors user defined color geoms. See 'Details' plot_grpfrq. dot.size Dot size, applies, argument type = \"line\". smooth.lines prints smooth line curve. applies, argument type = \"line\". grid.breaks numeric; sets distance breaks axis, .e. every grid.breaks'th position major grid printed. expand.grid logical, TRUE, plot grid expanded, .e. small margin axes plotting region. Default FALSE. ylim numeric vector length two, defining lower upper axis limits y scale. default, argument set NULL, .e. y-axis fits required range data. vjust character vector, indicating vertical position value labels. Allowed values vjust aesthetics ggplot2: \"left\", \"center\", \"right\", \"bottom\", \"middle\", \"top\" new options like \"inward\" \"outward\", align text towards away center plot respectively. hjust character vector, indicating horizontal position value labels. Allowed values vjust aesthetics ggplot2: \"left\", \"center\", \"right\", \"bottom\", \"middle\", \"top\" new options like \"inward\" \"outward\", align text towards away center plot respectively. y.offset numeric, offset text labels alignment adjusted top/bottom geom (see hjust vjust). coord.flip logical, TRUE, x y axis swapped.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_xtab.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot contingency tables — plot_xtab","text":"ggplot-object.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/plot_xtab.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot contingency tables — plot_xtab","text":"","code":"# create 4-category-items grp <- sample(1:4, 100, replace = TRUE) # create 3-category-items x <- sample(1:3, 100, replace = TRUE)  # plot \"cross tablulation\" of x and grp plot_xtab(x, grp)   # plot \"cross tablulation\" of x and y, including labels plot_xtab(x, grp, axis.labels = c(\"low\", \"mid\", \"high\"),          legend.labels = c(\"Grp 1\", \"Grp 2\", \"Grp 3\", \"Grp 4\"))   # plot \"cross tablulation\" of x and grp # as stacked proportional bars plot_xtab(x, grp, margin = \"row\", bar.pos = \"stack\",          show.summary = TRUE, coord.flip = TRUE) #> Warning: Chi-squared approximation may be incorrect   # example with vertical labels library(sjmisc) library(sjlabelled) data(efc) set_theme(geom.label.angle = 90) plot_xtab(efc$e42dep, efc$e16sex, vjust = \"center\", hjust = \"bottom\")   # grouped bars with EUROFAMCARE sample dataset # dataset was importet from an SPSS-file, # see ?sjmisc::read_spss data(efc) efc.val <- get_labels(efc) efc.var <- get_label(efc)  plot_xtab(efc$e42dep, efc$e16sex, title = efc.var['e42dep'],          axis.labels = efc.val[['e42dep']], legend.title = efc.var['e16sex'],          legend.labels = efc.val[['e16sex']])   plot_xtab(efc$e16sex, efc$e42dep, title = efc.var['e16sex'],          axis.labels = efc.val[['e16sex']], legend.title = efc.var['e42dep'],          legend.labels = efc.val[['e42dep']])   # ------------------------------- # auto-detection of labels works here # so no need to specify labels. For # title-auto-detection, use NULL # ------------------------------- plot_xtab(efc$e16sex, efc$e42dep, title = NULL)   plot_xtab(efc$e16sex, efc$e42dep, margin = \"row\",          bar.pos = \"stack\", coord.flip = TRUE)"},{"path":"https://strengejacke.github.io/sjPlot/reference/save_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Save ggplot-figure for print publication — save_plot","title":"Save ggplot-figure for print publication — save_plot","text":"Convenient function save last ggplot-figure                high quality publication.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/save_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Save ggplot-figure for print publication — save_plot","text":"","code":"save_plot(   filename,   fig = last_plot(),   width = 12,   height = 9,   dpi = 300,   theme = theme_get(),   label.color = \"black\",   label.size = 2.4,   axis.textsize = 0.8,   axis.titlesize = 0.75,   legend.textsize = 0.6,   legend.titlesize = 0.65,   legend.itemsize = 0.5 )"},{"path":"https://strengejacke.github.io/sjPlot/reference/save_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Save ggplot-figure for print publication — save_plot","text":"filename Name output file; filename must end one following accepted file types: \".png\", \".jpg\", \".svg\" \".tif\". fig plot saved. default, last plot saved. width Width figure, centimetres. height Height figure, centimetres. dpi Resolution dpi (dots per inch). Ignored vector formats, \".svg\". theme default theme use saving plot. label.color Color value labels (axis, plot, etc.). label.size Fontsize value labels inside plot area. axis.textsize Fontsize axis labels. axis.titlesize Fontsize axis titles. legend.textsize Fontsize legend labels. legend.titlesize Fontsize legend title. legend.itemsize Size legend's item (legend key), centimetres.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/save_plot.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Save ggplot-figure for print publication — save_plot","text":"convenient function default settings         come close needs fontsize scaling figures         saving printing publishing. uses cairographics         anti-aliasing (see png).                  adjusting plot appearance, see also sjPlot-themes.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/set_theme.html","id":null,"dir":"Reference","previous_headings":"","what":"Set global theme options for sjp-functions — set_theme","title":"Set global theme options for sjp-functions — set_theme","text":"Set global theme options sjp-functions.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/set_theme.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set global theme options for sjp-functions — set_theme","text":"","code":"set_theme(   base = theme_grey(),   theme.font = NULL,   title.color = \"black\",   title.size = 1.2,   title.align = \"left\",   title.vjust = NULL,   geom.outline.color = NULL,   geom.outline.size = 0,   geom.boxoutline.size = 0.5,   geom.boxoutline.color = \"black\",   geom.alpha = 1,   geom.linetype = 1,   geom.errorbar.size = 0.7,   geom.errorbar.linetype = 1,   geom.label.color = NULL,   geom.label.size = 4,   geom.label.alpha = 1,   geom.label.angle = 0,   axis.title.color = \"grey30\",   axis.title.size = 1.1,   axis.title.x.vjust = NULL,   axis.title.y.vjust = NULL,   axis.angle.x = 0,   axis.angle.y = 0,   axis.angle = NULL,   axis.textcolor.x = \"grey30\",   axis.textcolor.y = \"grey30\",   axis.textcolor = NULL,   axis.linecolor.x = NULL,   axis.linecolor.y = NULL,   axis.linecolor = NULL,   axis.line.size = 0.5,   axis.textsize.x = 1,   axis.textsize.y = 1,   axis.textsize = NULL,   axis.tickslen = NULL,   axis.tickscol = NULL,   axis.ticksmar = NULL,   axis.ticksize.x = NULL,   axis.ticksize.y = NULL,   panel.backcol = NULL,   panel.bordercol = NULL,   panel.col = NULL,   panel.major.gridcol = NULL,   panel.minor.gridcol = NULL,   panel.gridcol = NULL,   panel.gridcol.x = NULL,   panel.gridcol.y = NULL,   panel.major.linetype = 1,   panel.minor.linetype = 1,   plot.backcol = NULL,   plot.bordercol = NULL,   plot.col = NULL,   plot.margins = NULL,   legend.pos = \"right\",   legend.just = NULL,   legend.inside = FALSE,   legend.size = 1,   legend.color = \"black\",   legend.title.size = 1,   legend.title.color = \"black\",   legend.title.face = \"bold\",   legend.backgroundcol = \"white\",   legend.bordercol = \"white\",   legend.item.size = NULL,   legend.item.backcol = \"grey90\",   legend.item.bordercol = \"white\" )"},{"path":"https://strengejacke.github.io/sjPlot/reference/set_theme.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set global theme options for sjp-functions — set_theme","text":"base base theme theme built . default, metrics theme_gray() used. See 'Details'. theme.font base font family plot. title.color Color plot title. Default \"black\". title.size size plot title. Default 1.3. title.align alignment plot title. Must one \"left\" (default), \"center\" \"right\". may use initial letter . title.vjust numeric, vertical adjustment plot title. geom.outline.color Color geom outline. applies, geom.outline.size larger 0. geom.outline.size size bar outlines. Default 0.1. Use size 0 remove geom outline. geom.boxoutline.size size outlines median bar especially boxplots. Default 0.5. Use size 0 remove boxplot outline. geom.boxoutline.color Color outlines median bar especially boxplots. applies, geom.boxoutline.size larger 0. geom.alpha specifies transparancy (alpha value) geoms geom.linetype linetype line geoms. Default 1 (solid line). geom.errorbar.size size (thickness) error bars. Default 0.8 geom.errorbar.linetype linetype error bars. Default 1 (solid line). geom.label.color Color geom's value annotation labels geom.label.size size geom's value annotation labels geom.label.alpha alpha level geom's value annotation labels geom.label.angle angle geom's value annotation labels axis.title.color Color x- y-axis title labels axis.title.size size x- y-axis title labels axis.title.x.vjust numeric, vertical adjustment x-axis-title. axis.title.y.vjust numeric, vertical adjustment y-axis-title. axis.angle.x angle x-axis labels axis.angle.y angle y-axis labels axis.angle angle x- y-axis labels. set, overrides axis.angle.x axis.angle.y axis.textcolor.x Color x-axis labels. specified, default dark gray color palette used labels. axis.textcolor.y Color y-axis labels. specified, default dark gray color palette used labels. axis.textcolor Color x- y-axis labels. set, overrides axis.textcolor.x axis.textcolor.y axis.linecolor.x Color x-axis border axis.linecolor.y Color y-axis border axis.linecolor Color x- y-axis borders. set, overrides axis.linecolor.x axis.linecolor.y. axis.line.size size (thickness) axis lines. affected, axis.linecolor set. axis.textsize.x size x-axis labels axis.textsize.y size y-axis labels axis.textsize size x- y-axis labels. set, overrides axis.textsize.x axis.textsize.y. axis.tickslen length axis tick marks axis.tickscol Color axis tick marks axis.ticksmar margin axis labels tick marks axis.ticksize.x size tick marks x-axis. axis.ticksize.y size tick marks y-axis. panel.backcol Color diagram's background panel.bordercol Color whole diagram border (panel border) panel.col Color diagram's border background. set, overrides panel.bordercol panel.backcol. panel.major.gridcol Color major grid lines diagram background panel.minor.gridcol Color minor grid lines diagram background panel.gridcol Color minor major grid lines diagram background. set, overrides panel.major.gridcol panel.minor.gridcol. panel.gridcol.x See panel.gridcol. panel.gridcol.y See panel.gridcol. panel.major.linetype line type major grid lines panel.minor.linetype line type minor grid lines plot.backcol Color plot's background plot.bordercol Color whole plot's border (panel border) plot.col Color plot's region border background. set, overrides plot.backcol plot.bordercol. plot.margins numeric vector length 4, indicating top, right, bottom left margin plot region. legend.pos position legend, legend drawn. legend outside plot Use \"bottom\", \"top\", \"left\" \"right\"     position legend , , left right side     diagram. Right positioning default. legend inside plot legend.inside = TRUE, legend can placed inside     plot. Use \"top left\", \"top right\", \"bottom left\"     \"bottom right\" position legend corners,     two-element numeric vector values 0-1. See also     legend.inside. legend.just justification legend, relative position (\"center\" two-element numeric vector values 0-1. default (outside legend), justification centered. legend inside justification specified, legend justification set according legend position. legend.inside logical, use TRUE put legend inside plotting area. See legend.pos. legend.size text size legend. Default 1. Relative size, recommended values 0.3 2.5 legend.color Color legend labels legend.title.size text size legend title legend.title.color Color legend title legend.title.face font face legend title. default, \"bold\" face used. legend.backgroundcol fill color legend's background. Default \"white\", visible background drawn. legend.bordercol Color legend's border. Default \"white\", visible border drawn. legend.item.size size legend's item (legend key), centimetres. legend.item.backcol fill color legend's item-background. Default \"grey90\". legend.item.bordercol Color legend's item-border. Default \"white\".","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/set_theme.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Set global theme options for sjp-functions — set_theme","text":"customized theme object, NULL, ggplot-theme used.","code":""},{"path":[]},{"path":"https://strengejacke.github.io/sjPlot/reference/set_theme.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Set global theme options for sjp-functions — set_theme","text":"","code":"if (FALSE) { # \\dontrun{ library(sjmisc) data(efc) # set sjPlot-defaults, a slightly modification # of the ggplot base theme set_theme()  # legends of all plots inside set_theme(legend.pos = \"top left\", legend.inside = TRUE) plot_xtab(efc$e42dep, efc$e16sex)  # Use classic-theme. you may need to # load the ggplot2-library. library(ggplot2) set_theme(base = theme_classic()) plot_frq(efc$e42dep)  # adjust value labels set_theme(   geom.label.size = 3.5,   geom.label.color = \"#3366cc\",   geom.label.angle = 90 )  # hjust-aes needs adjustment for this update_geom_defaults('text', list(hjust = -0.1)) plot_xtab(efc$e42dep, efc$e16sex, vjust = \"center\", hjust = \"center\")  # Create own theme based on classic-theme set_theme(   base = theme_classic(), axis.linecolor = \"grey50\",   axis.textcolor = \"#6699cc\" ) plot_frq(efc$e42dep)} # }"},{"path":"https://strengejacke.github.io/sjPlot/reference/sjPlot-package.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Visualization for Statistics in Social Science — sjPlot-package","title":"Data Visualization for Statistics in Social Science — sjPlot-package","text":"Collection plotting table output functions data visualization. Results various statistical analyses (commonly used social sciences) can visualized using package, including simple cross tabulated frequencies, histograms, box plots, (generalized) linear models, mixed effects models, PCA correlation matrices, cluster analyses, scatter plots, Likert scales, effects plots interaction terms regression models, constructing index score variables much . package supports labelled data, .e. value variable labels labelled data (like vectors data frames) automatically used label output. labels can specified well. package ? short, functions package mostly two things: compute basic advanced statistical analyses either plot results ggplot-figure print html-table package help ? One challenging tasks working R get nicely formatted output statistical analyses, either graphical table format. sjPlot-package takes tasks makes easy create beautiful figures tables. many examples function related help files comprehensive online documentation https://strengejacke.github.io/sjPlot/. note package functions main functions follow specific naming conventions, hence starting specific prefix, indicates kind task functions perform. sjc - cluster analysis functions sjp - plotting functions sjt - (HTML) table output functions","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/sjPlot-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Data Visualization for Statistics in Social Science — sjPlot-package","text":"Daniel Lüdecke d.luedecke@uke.de","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/sjPlot-themes.html","id":null,"dir":"Reference","previous_headings":"","what":"Modify plot appearance — sjPlot-themes","title":"Modify plot appearance — sjPlot-themes","text":"Set default plot themes, use pre-defined color scales modify   plot table appearance.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/sjPlot-themes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Modify plot appearance — sjPlot-themes","text":"","code":"theme_sjplot(base_size = 12, base_family = \"\")  theme_sjplot2(base_size = 12, base_family = \"\")  theme_blank(base_size = 12, base_family = \"\")  theme_538(base_size = 12, base_family = \"\")  font_size(   title,   axis_title.x,   axis_title.y,   labels.x,   labels.y,   offset.x,   offset.y,   base.theme )  label_angle(angle.x, angle.y, base.theme)  legend_style(inside, pos, justify, base.theme)  scale_color_sjplot(palette = \"metro\", discrete = TRUE, reverse = FALSE, ...)  scale_fill_sjplot(palette = \"metro\", discrete = TRUE, reverse = FALSE, ...)  sjplot_pal(palette = \"metro\", n = NULL)  show_sjplot_pals()  css_theme(css.theme = \"regression\")"},{"path":"https://strengejacke.github.io/sjPlot/reference/sjPlot-themes.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Modify plot appearance — sjPlot-themes","text":"base_size Base font size. base_family Base font family. title Font size plot titles. axis_title.x Font size x-axis titles. axis_title.y Font size y-axis titles. labels.x Font size x-axis labels. labels.y Font size y-axis labels. offset.x Offset x-axis titles. offset.y Offset y-axis titles. base.theme Optional ggplot-theme-object, needed case multiple functions combined, e.g. theme_sjplot() + label_angle(). cases, use label_angle(base.theme = theme_sjplot()). angle.x Angle x-axis labels. angle.y Angle y-axis labels. inside Logical, use TRUE put legend inside plotting area. See also pos. pos Position legend, legend drawn. Legend outside plot Use \"bottom\", \"top\", \"left\" \"right\"     position legend , , left right side     diagram. Legend inside plot inside = TRUE, legend can placed inside     plot. Use \"top left\", \"top right\", \"bottom left\"     \"bottom right\" position legend corners,     two-element numeric vector values 0-1. See also     inside. justify Justification legend, relative position (\"center\" two-element numeric vector values 0-1. palette Character name color palette. discrete Logical, TRUE, discrete colour palette returned. Else, gradient palette returned, colours requested palette interpolated using colorRampPalette. reverse Logical, TRUE, order returned colours reversed. ... arguments passed ggplot's scale()-functions. n Numeric, number colors returned. default, complete colour palette returned. css.theme Name CSS pre-set theme-style. Can used table-functions.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/sjPlot-themes.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Modify plot appearance — sjPlot-themes","text":"using colors argument function calls (e.g.   plot_model()) calling one predefined scale-functions   (e.g. scale_color_sjplot()), pre-defined colour palettes   package. Use show_sjplot_pals() show available   colour palettes.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/sjPlot-themes.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Modify plot appearance — sjPlot-themes","text":"","code":"# prepare data if (requireNamespace(\"haven\")) { library(sjmisc) data(efc) efc <- to_factor(efc, c161sex, e42dep, c172code) m <- lm(neg_c_7 ~ pos_v_4 + c12hour + e42dep + c172code, data = efc)  # create plot-object p <- plot_model(m)  # change theme p + theme_sjplot()  # change font-size p + font_size(axis_title.x = 30)  # apply color theme p + scale_color_sjplot()  # show all available colour palettes show_sjplot_pals()  # get colour values from specific palette sjplot_pal(pal = \"breakfast club\") } #> Scale for colour is already present. #> Adding another scale for colour, which will replace the existing scale. #> [1] \"#b6411a\" \"#4182dd\" \"#2d6328\" \"#eec3d8\" \"#ecf0c8\""},{"path":"https://strengejacke.github.io/sjPlot/reference/sjp.aov1.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot One-Way-Anova tables — sjp.aov1","title":"Plot One-Way-Anova tables — sjp.aov1","text":"Plot One-Way-Anova table sum squares (SS) factor level (group)                dependent variable. SS factor variable                dependent variable (variance within groups) printed                model summary.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/sjp.aov1.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot One-Way-Anova tables — sjp.aov1","text":"","code":"sjp.aov1(   var.dep,   var.grp,   meansums = FALSE,   title = NULL,   axis.labels = NULL,   rev.order = FALSE,   string.interc = \"(Intercept)\",   axis.title = \"\",   axis.lim = NULL,   geom.colors = c(\"#3366a0\", \"#aa3333\"),   geom.size = 3,   wrap.title = 50,   wrap.labels = 25,   grid.breaks = NULL,   show.values = TRUE,   digits = 2,   y.offset = 0.15,   show.p = TRUE,   show.summary = FALSE )"},{"path":"https://strengejacke.github.io/sjPlot/reference/sjp.aov1.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot One-Way-Anova tables — sjp.aov1","text":"var.dep Dependent variable. used following formula: aov(var.dep ~ var.grp) var.grp Factor cross-classifying variable, var.dep grouped categories represented var.grp. meansums Logical, TRUE, values reported true group mean values. FALSE (default), values reported standard way, .e. values indicate difference group mean relation intercept (reference group). title character vector, used plot title. Depending plot type function, set automatically. title = \"\", title printed. effect-plots, may also character vector length > 1, define titles sub-plot facet. axis.labels character vector labels used axis labels. Optional argument, since cases, axis labels set automatically. rev.order Logical, TRUE, order categories (groups) reversed. string.interc Character vector indicates reference group (intercept), appended value label grouping variable. Default \"(Intercept)\". axis.title Character vector length one two (depending plot function type), used title(s) x y axis. specified, default labelling  chosen. Note: plot types may support argument sufficiently. cases, use returned ggplot-object add axis titles manually labs. Use axis.title = \"\" remove axis titles. axis.lim Numeric vector length 2, defining range plot axis. Depending plot type, may effect either x- y-axis, . multiple plot outputs (e.g., type = \"eff\" type = \"slope\" plot_model), axis.lim may also list vectors length 2, defining axis limits plot (non-faceted). geom.colors user defined color geoms. See 'Details' plot_grpfrq. geom.size size resp. width geoms (bar width, line thickness point size, depending plot type function). Note bar bin widths mostly need smaller values dot sizes. wrap.title numeric, determines many chars plot title displayed one line line break inserted. wrap.labels numeric, determines many chars value, variable axis labels displayed one line line break inserted. grid.breaks numeric; sets distance breaks axis, .e. every grid.breaks'th position major grid printed. show.values Logical, whether values plotted . digits Numeric, amount digits decimal point rounding estimates values. y.offset numeric, offset text labels alignment adjusted top/bottom geom (see hjust vjust). show.p Logical, adds significance levels values, value variable labels. show.summary logical, TRUE (default), summary chi-squared statistics (see chisq.test), Cramer's V Phi-value etc. shown. cell contains expected values lower five (lower 10 df 1), Fisher's exact test (see fisher.test) computed instead chi-squared test. table's matrix larger 2x2, Fisher's exact test Monte Carlo simulation computed.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/sjp.aov1.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot One-Way-Anova tables — sjp.aov1","text":"ggplot-object.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/sjp.aov1.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot One-Way-Anova tables — sjp.aov1","text":"","code":"data(efc) # note: \"var.grp\" does not need to be a factor. # coercion to factor is done by the function sjp.aov1(efc$c12hour, efc$e42dep) #> Warning: Continuous limits supplied to discrete scale. #> ℹ Did you mean `limits = factor(...)` or `scale_*_continuous()`?"},{"path":"https://strengejacke.github.io/sjPlot/reference/sjp.chi2.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot Pearson's Chi2-Test of multiple contingency tables — sjp.chi2","title":"Plot Pearson's Chi2-Test of multiple contingency tables — sjp.chi2","text":"Plot p-values Pearson's Chi2-tests multiple contingency tables ellipses tiles.                Requires data frame dichotomous (dummy) variables.                Calculation Chi2-matrix taken                Tales R.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/sjp.chi2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot Pearson's Chi2-Test of multiple contingency tables — sjp.chi2","text":"","code":"sjp.chi2(   df,   title = \"Pearson's Chi2-Test of Independence\",   axis.labels = NULL,   wrap.title = 50,   wrap.labels = 20,   show.legend = FALSE,   legend.title = NULL )"},{"path":"https://strengejacke.github.io/sjPlot/reference/sjp.chi2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot Pearson's Chi2-Test of multiple contingency tables — sjp.chi2","text":"df data frame (dichotomous) factor variables. title character vector, used plot title. Depending plot type function, set automatically. title = \"\", title printed. effect-plots, may also character vector length > 1, define titles sub-plot facet. axis.labels character vector labels used axis labels. Optional argument, since cases, axis labels set automatically. wrap.title numeric, determines many chars plot title displayed one line line break inserted. wrap.labels numeric, determines many chars value, variable axis labels displayed one line line break inserted. show.legend logical, TRUE, depending plot type function, legend added plot. legend.title character vector, used title plot legend.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/sjp.chi2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot Pearson's Chi2-Test of multiple contingency tables — sjp.chi2","text":"ggplot-object.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/sjp.chi2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot Pearson's Chi2-Test of multiple contingency tables — sjp.chi2","text":"","code":"# create data frame with 5 dichotomous (dummy) variables mydf <- data.frame(as.factor(sample(1:2, 100, replace=TRUE)),                    as.factor(sample(1:2, 100, replace=TRUE)),                    as.factor(sample(1:2, 100, replace=TRUE)),                    as.factor(sample(1:2, 100, replace=TRUE)),                    as.factor(sample(1:2, 100, replace=TRUE))) # create variable labels items <- list(c(\"Item 1\", \"Item 2\", \"Item 3\", \"Item 4\", \"Item 5\"))  # plot Chi2-contingency-table sjp.chi2(mydf, axis.labels = items)"},{"path":"https://strengejacke.github.io/sjPlot/reference/sjp.corr.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot correlation matrix — sjp.corr","title":"Plot correlation matrix — sjp.corr","text":"Plot correlation matrix ellipses tiles.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/sjp.corr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot correlation matrix — sjp.corr","text":"","code":"sjp.corr(   data,   title = NULL,   axis.labels = NULL,   sort.corr = TRUE,   decimals = 3,   na.deletion = c(\"listwise\", \"pairwise\"),   corr.method = c(\"pearson\", \"spearman\", \"kendall\"),   geom.colors = \"RdBu\",   wrap.title = 50,   wrap.labels = 20,   show.legend = FALSE,   legend.title = NULL,   show.values = TRUE,   show.p = TRUE,   p.numeric = FALSE )"},{"path":"https://strengejacke.github.io/sjPlot/reference/sjp.corr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot correlation matrix — sjp.corr","text":"data Matrix correlation coefficients returned cor-function, data.frame variables correlations columns computed. title character vector, used plot title. Depending plot type function, set automatically. title = \"\", title printed. effect-plots, may also character vector length > 1, define titles sub-plot facet. axis.labels character vector labels used axis labels. Optional argument, since cases, axis labels set automatically. sort.corr Logical, TRUE (default), axis labels sorted according correlation strength. FALSE, axis labels appear order variables included cor-computation data frame. decimals Indicates many decimal values comma printed values labels shown. Default 3. applies show.values = TRUE. na.deletion Indicates missing values treated. May either \"listwise\" (default) \"pairwise\". May abbreviated. corr.method Indicates correlation computation method. May one \"pearson\" (default), \"spearman\" \"kendall\". May abbreviated. geom.colors user defined color geoms. See 'Details' plot_grpfrq. wrap.title numeric, determines many chars plot title displayed one line line break inserted. wrap.labels numeric, determines many chars value, variable axis labels displayed one line line break inserted. show.legend logical, TRUE, depending plot type function, legend added plot. legend.title character vector, used title plot legend. show.values Logical, whether values plotted . show.p Logical, adds significance levels values, value variable labels. p.numeric Logical, TRUE, p-values printed numbers. FALSE (default), asterisks used.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/sjp.corr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot correlation matrix — sjp.corr","text":"(Insisibily) returns ggplot-object complete plot (plot) well data frame           used setting ggplot-object (df) original correlation matrix           (corr.matrix).","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/sjp.corr.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plot correlation matrix — sjp.corr","text":"Required argument either data.frame matrix correlation coefficients            returned cor-function. case ellipses,            ellipses size indicates strength correlation. Furthermore,            blue red colors indicate positive negative correlations,            stronger correlations darker.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/sjp.corr.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Plot correlation matrix — sjp.corr","text":"data matrix correlation coefficients returned       cor-function, p-values computed.       Thus, show.p p.numeric       effect data data.frame.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/sjp.poly.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot polynomials for (generalized) linear regression — sjp.poly","title":"Plot polynomials for (generalized) linear regression — sjp.poly","text":"function plots scatter plot term poly.term                response variable x adds - depending                amount numeric values poly.degree - multiple                polynomial curves. loess-smoothed line can added see                polynomial curves fits best data.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/sjp.poly.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot polynomials for (generalized) linear regression — sjp.poly","text":"","code":"sjp.poly(   x,   poly.term,   poly.degree,   poly.scale = FALSE,   fun = NULL,   axis.title = NULL,   geom.colors = NULL,   geom.size = 0.8,   show.loess = TRUE,   show.loess.ci = TRUE,   show.p = TRUE,   show.scatter = TRUE,   point.alpha = 0.2,   point.color = \"#404040\",   loess.color = \"#808080\" )"},{"path":"https://strengejacke.github.io/sjPlot/reference/sjp.poly.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot polynomials for (generalized) linear regression — sjp.poly","text":"x vector, representing response variable linear (mixed) model; linear (mixed) model returned lm lmer. poly.term x vector, poly.term also vector, representing polynomial term (independent variabl) model; x fitted model, poly.term polynomial term's name character string. See 'Examples'. poly.degree Numeric, numeric vector, indicating degree polynomial. poly.degree numeric vector, multiple polynomial curves degree plotted. See 'Examples'. poly.scale Logical, TRUE, poly.term scaled linear regression computed. Default FALSE. Scaling polynomial term may impact resulting p-values. fun Linear function modelling polynomial terms. Use fun = \"lm\" linear models, fun = \"glm\" generalized linear models. x vector, fitted model object, function detected automatically. x vector, fun defaults \"lm\". axis.title Character vector length one two (depending plot function type), used title(s) x y axis. specified, default labelling  chosen. Note: plot types may support argument sufficiently. cases, use returned ggplot-object add axis titles manually labs. Use axis.title = \"\" remove axis titles. geom.colors user defined color geoms. See 'Details' plot_grpfrq. geom.size size resp. width geoms (bar width, line thickness point size, depending plot type function). Note bar bin widths mostly need smaller values dot sizes. show.loess Logical, TRUE, additional loess-smoothed line plotted. show.loess.ci Logical, TRUE, confidence region loess-smoothed line plotted. show.p Logical, TRUE (default), p-values polynomial terms printed console. show.scatter Logical, TRUE (default), adds scatter plot data points plot. point.alpha Alpha value point-geoms scatter plots. applies, show.scatter = TRUE. point.color Color point-geoms scatter plots. applies, show.scatter = TRUE. loess.color Color loess-smoothed line. applies, show.loess = TRUE.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/sjp.poly.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot polynomials for (generalized) linear regression — sjp.poly","text":"ggplot-object.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/sjp.poly.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plot polynomials for (generalized) linear regression — sjp.poly","text":"polynomial degree, simple linear regression x (resp.            extracted response, x fitted model) performed,            polynomial term poly.term included independent variable.            Thus, lm(y ~ x + (x^2) + ... + (x^)) repeatedly computed            values poly.degree, predicted values            reponse plotted raw values poly.term.            x fitted model, covariates ignored            finding best fitting polynomial.             function evaluates raw polynomials, orthogonal polynomials.            Polynomials computed using poly function,            argument raw = TRUE.             find polynomial degree fits best data, loess-smoothed            line (dark grey) can added (show.loess = TRUE). polynomial curves            comes closest loess-smoothed line best            fit data.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/sjp.poly.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot polynomials for (generalized) linear regression — sjp.poly","text":"","code":"library(sjmisc) data(efc) # linear fit. loess-smoothed line indicates a more # or less cubic curve sjp.poly(efc$c160age, efc$quol_5, 1) #> Polynomial degrees: 1 #> --------------------- #> p(x^1): 0.000 #>  #> `geom_smooth()` using formula = 'y ~ x'   # quadratic fit sjp.poly(efc$c160age, efc$quol_5, 2) #> Polynomial degrees: 2 #> --------------------- #> p(x^1): 0.078 #> p(x^2): 0.533 #>  #> `geom_smooth()` using formula = 'y ~ x'   # linear to cubic fit sjp.poly(efc$c160age, efc$quol_5, 1:4, show.scatter = FALSE) #> Polynomial degrees: 1 #> --------------------- #> p(x^1): 0.000 #>  #> Polynomial degrees: 2 #> --------------------- #> p(x^1): 0.078 #> p(x^2): 0.533 #>  #> Polynomial degrees: 3 #> --------------------- #> p(x^1): 0.012 #> p(x^2): 0.001 #> p(x^3): 0.000 #>  #> Polynomial degrees: 4 #> --------------------- #> p(x^1): 0.777 #> p(x^2): 0.913 #> p(x^3): 0.505 #> p(x^4): 0.254 #>  #> `geom_smooth()` using formula = 'y ~ x'    # fit sample model fit <- lm(tot_sc_e ~ c12hour + e17age + e42dep, data = efc) # inspect relationship between predictors and response plot_model(fit, type = \"slope\") #> `geom_smooth()` using formula = 'y ~ x' #> `geom_smooth()` using formula = 'y ~ x' #> Warning: pseudoinverse used at 4.015 #> Warning: neighborhood radius 2.015 #> Warning: reciprocal condition number  1.5462e-16 #> Warning: There are other near singularities as well. 1  # \"e17age\" does not seem to be linear correlated to response # try to find appropiate polynomial. Grey line (loess smoothed) # indicates best fit. Looks like x^4 has the best fit, # however, only x^3 has significant p-values. sjp.poly(fit, \"e17age\", 2:4, show.scatter = FALSE) #> Polynomial degrees: 2 #> --------------------- #> p(x^1): 0.734 #> p(x^2): 0.721 #>  #> Polynomial degrees: 3 #> --------------------- #> p(x^1): 0.010 #> p(x^2): 0.011 #> p(x^3): 0.011 #>  #> Polynomial degrees: 4 #> --------------------- #> p(x^1): 0.234 #> p(x^2): 0.267 #> p(x^3): 0.303 #> p(x^4): 0.343 #>  #> `geom_smooth()` using formula = 'y ~ x'   if (FALSE) { # \\dontrun{ # fit new model fit <- lm(tot_sc_e ~ c12hour + e42dep + e17age + I(e17age^2) + I(e17age^3),           data = efc) # plot marginal effects of polynomial term plot_model(fit, type = \"pred\", terms = \"e17age\")} # }"},{"path":"https://strengejacke.github.io/sjPlot/reference/sjplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Wrapper to create plots and tables within a pipe-workflow — sjplot","title":"Wrapper to create plots and tables within a pipe-workflow — sjplot","text":"function pipe-friendly argument-structure,              first argument always data, followed variables              plotted printed table. function transforms              input calls requested sjp.- resp. sjt.-function              create plot table.               sjplot() sjtab() support grouped data frames.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/sjplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Wrapper to create plots and tables within a pipe-workflow — sjplot","text":"","code":"sjplot(data, ..., fun = c(\"grpfrq\", \"xtab\", \"aov1\", \"likert\"))  sjtab(data, ..., fun = c(\"xtab\", \"stackfrq\"))"},{"path":"https://strengejacke.github.io/sjPlot/reference/sjplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Wrapper to create plots and tables within a pipe-workflow — sjplot","text":"data data frame. May also grouped data frame (see 'Note' 'Examples'). ... Names variables plotted, also arguments passed sjPlot-functions. See 'Examples'. fun Plotting function. Refers function name sjPlot-functions. See 'Details' 'Examples'.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/sjplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Wrapper to create plots and tables within a pipe-workflow — sjplot","text":"See related sjp. sjt.-functions.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/sjplot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Wrapper to create plots and tables within a pipe-workflow — sjplot","text":"Following fun-values currently supported: \"aov1\" calls sjp.aov1. first             two variables data used (required) create plot. \"grpfrq\" calls plot_grpfrq. first             two variables data used (required) create plot. \"likert\" calls plot_likert. data             must data frame items plot. \"stackfrq\" calls tab_stackfrq.             data must data frame items create table. \"xtab\" calls plot_xtab tab_xtab.             first two variables data used (required)             create plot table.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/sjplot.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Wrapper to create plots and tables within a pipe-workflow — sjplot","text":"...-argument used, first, specify variables data       plotted, , second, name arguments       used subsequent plotting functions. Refer online-help       supported plotting-functions see valid arguments.       data may also grouped data frame (see group_by)       two grouping variables. Plots created subgroup .","code":""},{"path":[]},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_corr.html","id":null,"dir":"Reference","previous_headings":"","what":"Summary of correlations as HTML table — tab_corr","title":"Summary of correlations as HTML table — tab_corr","text":"Shows results computed correlation HTML table. Requires either                data.frame matrix correlation coefficients                returned cor-function.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_corr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summary of correlations as HTML table — tab_corr","text":"","code":"tab_corr(   data,   na.deletion = c(\"listwise\", \"pairwise\"),   corr.method = c(\"pearson\", \"spearman\", \"kendall\"),   title = NULL,   var.labels = NULL,   wrap.labels = 40,   show.p = TRUE,   p.numeric = FALSE,   fade.ns = TRUE,   val.rm = NULL,   digits = 3,   triangle = \"both\",   string.diag = NULL,   CSS = NULL,   encoding = NULL,   file = NULL,   use.viewer = TRUE,   remove.spaces = TRUE )"},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_corr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summary of correlations as HTML table — tab_corr","text":"data Matrix correlation coefficients returned cor-function, data.frame variables correlations columns computed. na.deletion Indicates missing values treated. May either \"listwise\" (default) \"pairwise\". May abbreviated. corr.method Indicates correlation computation method. May one \"pearson\" (default), \"spearman\" \"kendall\". May abbreviated. title String, used table caption. var.labels Character vector variable names, used label variables output. wrap.labels Numeric, determines many chars value, variable axis labels displayed one line line break inserted. show.p Logical, TRUE, p-values also printed. p.numeric Logical, TRUE, p-values printed numbers. FALSE (default), asterisks used. fade.ns Logical, TRUE (default), non-significant correlation-values appear faded (using lighter grey text color). See 'Note'. val.rm Specify number 0 1 suppress output correlation values smaller val.rm. absolute correlation values used, correlation value -.5 greater val.rm = .4 thus omitted. default, argument NULL, hence values shown table. correlation value specified value val.rm, still printed HTML table, made \"invisible\" white foreground color. can use CSS argument (\"css.valueremove\") change color appearance correlation value smaller limit specified val.rm. digits Amount decimals estimates triangle Indicates whether upper right (use \"upper\"), lower left (use \"lower\") (use \"\") triangles correlation table filled values. Default \"\". can specifiy inital letter . string.diag vector string values length ncol(data) (number correlated items) can used display content diagonal cells row column item identical (.e. \"self-correlation\"). defauilt, argument NULL diagnal cells empty. CSS list user-defined style-sheet-definitions, according official CSS syntax. See 'Details' package-vignette. encoding Character vector, indicating charset encoding used variable value labels. Default \"UTF-8\". Windows Systems, encoding = \"Windows-1252\" might necessary proper display special characters. file Destination file, output saved file. NULL (default), output saved temporary file opened either IDE's viewer pane default web browser. use.viewer Logical, TRUE, HTML table shown IDE's viewer pane. FALSE viewer available, HTML table opened web browser. remove.spaces Logical, TRUE, leading spaces removed lines final string contains html-data. Use , want remove parantheses html-tags. html-source may look less pretty, may help exporting html-tables office tools.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_corr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summary of correlations as HTML table — tab_corr","text":"Invisibly returns web page style sheet (page.style), web page content (page.content), complete html-output (page.complete) html-table inline-css use knitr (knitr) use.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_corr.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Summary of correlations as HTML table — tab_corr","text":"data matrix correlation coefficients returned       cor-function, p-values computed.       Thus, show.p, p.numeric fade.ns       effect data data.frame.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_corr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summary of correlations as HTML table — tab_corr","text":"","code":"if (FALSE) { # \\dontrun{ if (interactive()) {   # Data from the EUROFAMCARE sample dataset   library(sjmisc)   data(efc)    # retrieve variable and value labels   varlabs <- get_label(efc)    # recveive first item of COPE-index scale   start <- which(colnames(efc) == \"c83cop2\")   # recveive last item of COPE-index scale   end <- which(colnames(efc) == \"c88cop7\")    # create data frame with COPE-index scale   mydf <- data.frame(efc[, c(start:end)])   colnames(mydf) <- varlabs[c(start:end)]    # we have high correlations here, because all items   # belong to one factor.   tab_corr(mydf, p.numeric = TRUE)    # auto-detection of labels, only lower triangle   tab_corr(efc[, c(start:end)], triangle = \"lower\")    # auto-detection of labels, only lower triangle, all correlation   # values smaller than 0.3 are not shown in the table   tab_corr(efc[, c(start:end)], triangle = \"lower\", val.rm = 0.3)    # auto-detection of labels, only lower triangle, all correlation   # values smaller than 0.3 are printed in blue   tab_corr(efc[, c(start:end)], triangle = \"lower\",val.rm = 0.3,            CSS = list(css.valueremove = 'color:blue;')) }} # }"},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_df.html","id":null,"dir":"Reference","previous_headings":"","what":"Print data frames as HTML table. — tab_df","title":"Print data frames as HTML table. — tab_df","text":"functions print data frames HTML-table, showing   results RStudio's viewer pane web browser.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_df.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print data frames as HTML table. — tab_df","text":"","code":"tab_df(   x,   title = NULL,   footnote = NULL,   col.header = NULL,   show.type = FALSE,   show.rownames = FALSE,   show.footnote = FALSE,   alternate.rows = FALSE,   sort.column = NULL,   digits = 2,   encoding = \"UTF-8\",   CSS = NULL,   file = NULL,   use.viewer = TRUE,   ... )  tab_dfs(   x,   titles = NULL,   footnotes = NULL,   col.header = NULL,   show.type = FALSE,   show.rownames = FALSE,   show.footnote = FALSE,   alternate.rows = FALSE,   sort.column = NULL,   digits = 2,   encoding = \"UTF-8\",   CSS = NULL,   file = NULL,   use.viewer = TRUE,   rnames = NULL,   ... )"},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_df.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print data frames as HTML table. — tab_df","text":"x tab_df(), data frame; tab_dfs(), list data frames. title, titles, footnote, footnotes Character vector table caption(s) resp. footnote(s).  tab_df(), must character length 1; tab_dfs(), character vector length x (.e. one title footnote per data frame). col.header Character vector elements used column header table. NULL, column names x used column header. show.type Logical, TRUE, adds information variable type variable column. show.rownames Logical, TRUE, adds column data frame's rowname table output. show.footnote Logical, TRUE,adds summary footnote table. tab_df(), specify string footnote, tab_dfs() provide character vector footnotes. alternate.rows Logical, TRUE, rows printed alternatig colors (white light grey default). sort.column Numeric vector, indicating index column sorted. default, column sorted ascending order. Use negative index descending order, instance, sort.column = -3 sort third column descending order. Note first column rownames counted. digits Numeric, amount digits decimal point rounding values. encoding Character vector, indicating charset encoding used variable value labels. Default \"UTF-8\". Windows Systems, encoding = \"Windows-1252\" might necessary proper display special characters. CSS list user-defined style-sheet-definitions, according official CSS syntax. See 'Details' package-vignette. file Destination file, output saved file. NULL (default), output saved temporary file opened either IDE's viewer pane default web browser. use.viewer Logical, TRUE, HTML table shown IDE's viewer pane. FALSE viewer available, HTML table opened web browser. ... Currently used. rnames Character vector, can used set row names show.rownames=TRUE.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_df.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print data frames as HTML table. — tab_df","text":"list following items: web page style sheet (page.style), HTML content data frame (page.content), complete HTML page, including header, style sheet body (page.complete) HTML table inline-css use knitr (knitr) file path, HTML page saved disk (file)","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_df.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Print data frames as HTML table. — tab_df","text":"use CSS-argument?      CSS-argument, visual appearance tables   can modified. get overview style-sheet-classnames   used function, see return value page.style   details. Arguments list following syntax: class-name argument name style-definition must end semicolon can add style information default styles using +   (plus-sign) initial character argument attributes.   Examples: table = 'border:2px solid red;' solid 2-pixel table border red. summary = 'font-weight:bold;' bold fontweight summary row. lasttablerow = 'border-bottom: 1px dotted blue;' blue dotted border last table row. colnames = '+color:green' add green color formatting column names. arc = 'color:blue;' blue text color 2nd row. caption = '+color:red;' add red font-color default table caption style. See examples package-vignette.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_df.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Print data frames as HTML table. — tab_df","text":"HTML tables can either saved file manually opened   (use argument file) can saved temporary files   displayed RStudio Viewer pane (working RStudio)   opened default web browser. Displaying resp. opening   temporary file default behaviour.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_df.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Print data frames as HTML table. — tab_df","text":"","code":"if (FALSE) { # \\dontrun{ data(iris) data(mtcars) tab_df(iris[1:5, ]) tab_dfs(list(iris[1:5, ], mtcars[1:5, 1:5]))  # sort 2nd column ascending tab_df(iris[1:5, ], sort.column = 2)  # sort 2nd column descending tab_df(iris[1:5, ], sort.column = -2)} # }"},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_fa.html","id":null,"dir":"Reference","previous_headings":"","what":"Summary of factor analysis as HTML table — tab_fa","title":"Summary of factor analysis as HTML table — tab_fa","text":"Performs factor analysis data frame matrix                displays factors HTML                table, saves file.  case data frame used                parameter, Cronbach's Alpha value factor scale calculated,                .e. variables highest loading factor taken                reliability test. result alpha value factor dimension.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_fa.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summary of factor analysis as HTML table — tab_fa","text":"","code":"tab_fa(   data,   rotation = \"promax\",   method = c(\"ml\", \"minres\", \"wls\", \"gls\", \"pa\", \"minchi\", \"minrank\"),   nmbr.fctr = NULL,   fctr.load.tlrn = 0.1,   sort = FALSE,   title = \"Factor Analysis\",   var.labels = NULL,   wrap.labels = 40,   show.cronb = TRUE,   show.comm = FALSE,   alternate.rows = FALSE,   digits = 2,   CSS = NULL,   encoding = NULL,   file = NULL,   use.viewer = TRUE,   remove.spaces = TRUE )"},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_fa.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summary of factor analysis as HTML table — tab_fa","text":"data data frame used compute PCA, prcomp object. rotation Rotation factor loadings. May one \"varimax\", \"quartimax\", \"promax\", \"oblimin\", \"simplimax\", \"cluster\" \"none\". method factoring method used. \"ml\" maximum likelihood factor analysis (default). \"minres\" minimum residual (OLS), \"wls\" weighted least squares (WLS) solution, \"gls\" generalized weighted least squares (GLS), \"pa\" principal factor solution, \"minchi\" minimize sample size weighted chi square treating pairwise correlations different number subjects per pair. \"minrank\" minimum rank factor analysis. nmbr.fctr Number factors used calculating rotation. default, value NULL amount factors calculated according Kaiser-criteria. fctr.load.tlrn Specifies minimum difference variable needs factor loadings (components) order indicate clear loading just one factor diffusing factors. instance, variable 0.8, 0.82 0.84 factor loading 3 possible factors can clearly assigned just one factor thus removed principal component analysis. default, minimum difference loading values highest 2nd highest factor 0.1 sort logical, TRUE, sort loadings factors (items sorted terms greatest loading, descending order) title String, used table caption. var.labels Character vector variable names, used label variables output. wrap.labels Numeric, determines many chars value, variable axis labels displayed one line line break inserted. show.cronb Logical, TRUE (default), cronbach's alpha value factor scale calculated, .e. variables highest loading factor taken reliability test. result alpha value factor dimension. applies data data frame. show.comm Logical, TRUE, show communality column table. alternate.rows Logical, TRUE, rows printed alternatig colors (white light grey default). digits Amount decimals estimates CSS list user-defined style-sheet-definitions, according official CSS syntax. See 'Details' package-vignette. encoding Character vector, indicating charset encoding used variable value labels. Default \"UTF-8\". Windows Systems, encoding = \"Windows-1252\" might necessary proper display special characters. file Destination file, output saved file. NULL (default), output saved temporary file opened either IDE's viewer pane default web browser. use.viewer Logical, TRUE, HTML table shown IDE's viewer pane. FALSE viewer available, HTML table opened web browser. remove.spaces Logical, TRUE, leading spaces removed lines final string contains html-data. Use , want remove parantheses html-tags. html-source may look less pretty, may help exporting html-tables office tools.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_fa.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summary of factor analysis as HTML table — tab_fa","text":"Invisibly returns web page style sheet (page.style), web page content (page.content), complete html-output (page.complete), html-table inline-css use knitr (knitr), factor.index, .e. column index variable highest factor loading factor removed.items, .e. variables removed outside fctr.load.tlrn's range. use.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_fa.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Summary of factor analysis as HTML table — tab_fa","text":"method factor analysis relies functions       fa fa.parallel psych package.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_fa.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summary of factor analysis as HTML table — tab_fa","text":"","code":"if (FALSE) { # \\dontrun{ # Data from the EUROFAMCARE sample dataset library(sjmisc) library(GPArotation) data(efc)  # recveive first item of COPE-index scale start <- which(colnames(efc) == \"c82cop1\") # recveive last item of COPE-index scale end <- which(colnames(efc) == \"c90cop9\") # auto-detection of labels if (interactive()) {   tab_fa(efc[, start:end]) }} # }"},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_itemscale.html","id":null,"dir":"Reference","previous_headings":"","what":"Summary of item analysis of an item scale as HTML table — tab_itemscale","title":"Summary of item analysis of an item scale as HTML table — tab_itemscale","text":"function performs item analysis certain statistics                useful scale index development. resulting tables shown                viewer pane resp. webbrowser can saved file. Following statistics                computed item data frame: percentage missing values mean value standard deviation skew item difficulty item discrimination Cronbach's Alpha item removed scale mean (average) inter-item-correlation Optional, following statistics can computed well: kurstosis Shapiro-Wilk Normality Test factor.groups NULL, data frame df                splitted groups, assuming factor.groups indicate columns                data frame belong certain factor (see return value function tab_pca                example retrieving factor groups scale see examples details).","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_itemscale.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summary of item analysis of an item scale as HTML table — tab_itemscale","text":"","code":"tab_itemscale(   df,   factor.groups = NULL,   factor.groups.titles = \"auto\",   scale = FALSE,   min.valid.rowmean = 2,   alternate.rows = TRUE,   sort.column = NULL,   show.shapiro = FALSE,   show.kurtosis = FALSE,   show.corr.matrix = TRUE,   CSS = NULL,   encoding = NULL,   file = NULL,   use.viewer = TRUE,   remove.spaces = TRUE )  sjt.itemanalysis(   df,   factor.groups = NULL,   factor.groups.titles = \"auto\",   scale = FALSE,   min.valid.rowmean = 2,   alternate.rows = TRUE,   sort.column = NULL,   show.shapiro = FALSE,   show.kurtosis = FALSE,   show.corr.matrix = TRUE,   CSS = NULL,   encoding = NULL,   file = NULL,   use.viewer = TRUE,   remove.spaces = TRUE )"},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_itemscale.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summary of item analysis of an item scale as HTML table — tab_itemscale","text":"df data frame items. factor.groups NULL, df splitted sub-groups, item analysis carried groups. Must vector length ncol(df), item vector represents group number related columns df. factor.groups = \"auto\", principal component analysis Varimax rotation performed, resulting groups components used group index. See 'Examples'. factor.groups.titles Titles factor group used table caption component-table. Must character vector length length(unique(factor.groups)). Default \"auto\", means table standard caption Component x. Use NULL suppress table captions. scale Logical, TRUE, data frame's vectors scaled calculating Cronbach's Alpha value (see item_reliability). Recommended, variables different measures / scales. min.valid.rowmean Minimum amount valid values compute row means index scores. Default 2, .e. return values index.scores df.index.scores computed items least min.valid.rowmean per case (observation, technically, row). See mean_n details. alternate.rows Logical, TRUE, rows printed alternatig colors (white light grey default). sort.column Numeric vector, indicating index column sorted. default, column sorted ascending order. Use negative index descending order, instance, sort.column = -3 sort third column descending order. Note first column rownames counted. show.shapiro Logical, TRUE, Shapiro-Wilk normality test computed item. See shapiro.test details. show.kurtosis Logical, TRUE, kurtosis item also shown (see kurtosi describe psych-package details. show.corr.matrix Logical, TRUE (default), correlation matrix component's index score shown. applies factor.groups NULL df one group. First, case (df's row), sum variables (df's columns) scaled (using scale-function) represents \"total score\" component (component represented group factor.groups). , case (df's row) scales sum score component. Finally, correlation \"scale sum scores\" computed. CSS list user-defined style-sheet-definitions, according official CSS syntax. See 'Details' package-vignette. encoding Character vector, indicating charset encoding used variable value labels. Default \"UTF-8\". Windows Systems, encoding = \"Windows-1252\" might necessary proper display special characters. file Destination file, output saved file. NULL (default), output saved temporary file opened either IDE's viewer pane default web browser. use.viewer Logical, TRUE, HTML table shown IDE's viewer pane. FALSE viewer available, HTML table opened web browser. remove.spaces Logical, TRUE, leading spaces removed lines final string contains html-data. Use , want remove parantheses html-tags. html-source may look less pretty, may help exporting html-tables office tools.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_itemscale.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summary of item analysis of an item scale as HTML table — tab_itemscale","text":"Invisibly returns df.list: List data frames item analysis sub.group (complete, factor.groups NULL) index.scores: data frame standardized scale / index scores case (mean value scale items case) sub-group. ideal.item.diff: List vectors indicate ideal item difficulty item sub-group. Item difficulty differs items different levels. cronbach.values: List Cronbach's Alpha values overall item scale sub-group. knitr.list: List html-tables inline-css use knitr table (sub-group) knitr: html-table complete output inline-css use knitr complete.page: Complete html-output. factor.groups = NULL, list contains one elment, since just one          table printed complete scale indicated df. factor.groups          vector group-index-values, lists contain elements sub-group.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_itemscale.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Summary of item analysis of an item scale as HTML table — tab_itemscale","text":"Shapiro-Wilk Normality Test (see column W(p)) tests item distribution significantly different normal. Item difficulty range 0.2 0.8. Ideal value p+(1-p)/2 (mostly 0.5 0.8). item discrimination, acceptable values 0.20 higher; closer 1.00 better. See item_reliability details. case total Cronbach's Alpha value acceptable cut-0.7 (mostly index items), mean inter-item-correlation alternative measure indicate acceptability. Satisfactory range lies 0.2 0.4. See also item_intercor.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_itemscale.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Summary of item analysis of an item scale as HTML table — tab_itemscale","text":"Jorion N, Self B, James K, Schroeder L, DiBello L, Pellegrino J (2013) Classical Test Theory Analysis Dynamics Concept Inventory. (web) Briggs SR, Cheek JM (1986) role factor analysis development evaluation personality scales. Journal Personality, 54(1), 106-148. doi: 10.1111/j.1467-6494.1986.tb00391.x McLean S et al. (2013) Stigmatizing attitudes beliefs bulimia nervosa: Gender, age, education income variability community sample. International Journal Eating Disorders. doi: 10.1002/eat.22227 Trochim WMK (2008) Types Reliability.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_itemscale.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summary of item analysis of an item scale as HTML table — tab_itemscale","text":"","code":"# Data from the EUROFAMCARE sample dataset library(sjmisc) library(sjlabelled) data(efc)  # retrieve variable and value labels varlabs <- get_label(efc)  # recveive first item of COPE-index scale start <- which(colnames(efc) == \"c82cop1\") # recveive last item of COPE-index scale end <- which(colnames(efc) == \"c90cop9\")  # create data frame with COPE-index scale mydf <- data.frame(efc[, start:end]) colnames(mydf) <- varlabs[start:end]  if (FALSE) { # \\dontrun{ if (interactive()) {   tab_itemscale(mydf)    # auto-detection of labels   tab_itemscale(efc[, start:end])    # Compute PCA on Cope-Index, and perform a   # item analysis for each extracted factor.   indices <- tab_pca(mydf)$factor.index   tab_itemscale(mydf, factor.groups = indices)    # or, equivalent   tab_itemscale(mydf, factor.groups = \"auto\") }} # }"},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_model.html","id":null,"dir":"Reference","previous_headings":"","what":"Print regression models as HTML table — tab_model","title":"Print regression models as HTML table — tab_model","text":"tab_model() creates HTML tables regression models.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print regression models as HTML table — tab_model","text":"","code":"tab_model(   ...,   transform,   show.intercept = TRUE,   show.est = TRUE,   show.ci = 0.95,   show.ci50 = FALSE,   show.se = NULL,   show.std = NULL,   std.response = TRUE,   show.p = TRUE,   show.stat = FALSE,   show.df = FALSE,   show.zeroinf = TRUE,   show.r2 = TRUE,   show.icc = TRUE,   show.re.var = TRUE,   show.ngroups = TRUE,   show.fstat = FALSE,   show.aic = FALSE,   show.aicc = FALSE,   show.dev = FALSE,   show.loglik = FALSE,   show.obs = TRUE,   show.reflvl = FALSE,   terms = NULL,   rm.terms = NULL,   order.terms = NULL,   keep = NULL,   drop = NULL,   title = NULL,   pred.labels = NULL,   dv.labels = NULL,   wrap.labels = 25,   bootstrap = FALSE,   iterations = 1000,   seed = NULL,   robust = FALSE,   vcov.fun = NULL,   vcov.type = NULL,   vcov.args = NULL,   string.pred = \"Predictors\",   string.est = \"Estimate\",   string.std = \"std. Beta\",   string.ci = \"CI\",   string.se = \"std. Error\",   string.std_se = \"standardized std. Error\",   string.std_ci = \"standardized CI\",   string.p = \"p\",   string.std.p = \"std. p\",   string.df = \"df\",   string.stat = \"Statistic\",   string.std.stat = \"std. Statistic\",   string.resp = \"Response\",   string.intercept = \"(Intercept)\",   strings = NULL,   ci.hyphen = \"&nbsp;&ndash;&nbsp;\",   minus.sign = \"&#45;\",   collapse.ci = FALSE,   collapse.se = FALSE,   linebreak = TRUE,   col.order = c(\"est\", \"se\", \"std.est\", \"std.se\", \"ci\", \"std.ci\", \"ci.inner\", \"ci.outer\",     \"stat\", \"std.stat\", \"p\", \"std.p\", \"df.error\", \"response.level\"),   digits = 2,   digits.p = 3,   digits.rsq = 3,   digits.re = 2,   emph.p = TRUE,   p.val = NULL,   df.method = NULL,   p.style = c(\"numeric\", \"stars\", \"numeric_stars\", \"scientific\", \"scientific_stars\"),   p.threshold = c(0.05, 0.01, 0.001),   p.adjust = NULL,   case = \"parsed\",   auto.label = TRUE,   prefix.labels = c(\"none\", \"varname\", \"label\"),   bpe = \"median\",   CSS = css_theme(\"regression\"),   file = NULL,   use.viewer = TRUE,   encoding = \"UTF-8\" )"},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print regression models as HTML table — tab_model","text":"... One regression models, including glm's mixed models. May also list fitted models. See 'Examples'. transform character vector, naming function applied estimates confidence intervals. default, transform automatically use \"exp\" transformation applicable classes model (e.g. logistic poisson regression). Estimates linear models remain untransformed. Use NULL want raw, non-transformed estimates. show.intercept Logical, TRUE, intercepts printed. show.est Logical, TRUE, estimates printed. show.ci Either logical, TRUE, confidence intervals printed table; FALSE, confidence intervals omitted. numeric, 0 1, indicating range confidence intervals. show.ci50 Logical, TRUE, Bayesian models, second credible interval added table output. show.se Logical, TRUE, standard errors also printed. robust standard errors required, use arguments vcov.fun, vcov.type vcov.args (see standard_error details). show.std Indicates whether standardized beta-coefficients also printed, yes, type standardization done. See 'Details'. std.response Logical, whether response variable also standardized standardized coefficients requested. Setting std.response = TRUE show.std = TRUE behave complete data standardized fitting model. show.p Logical, TRUE, p-values also printed. show.stat Logical, TRUE, coefficients' test statistic also printed. show.df Logical, TRUE p.val = \"kr\", p-values linear mixed models based df Kenward-Rogers approximation. df-values printed. See p_value details. show.zeroinf Logical, TRUE model zero-inflated model part, also printed table. show.r2 Logical, TRUE, r-squared value also printed. Depending model, might pseudo-r-squared values, Bayesian r-squared etc. See r2 details. show.icc Logical, TRUE, prints intraclass correlation coefficient mixed models. See icc details. show.re.var Logical, TRUE, prints random effect variances mixed models. See get_variance details. show.ngroups Logical, TRUE, shows number random effects groups mixed models. show.fstat Logical, TRUE, F-statistics model printed table summary. option supported model types. show.aic Logical, TRUE, AIC value model printed table summary. show.aicc Logical, TRUE, second-order AIC value model printed table summary. show.dev Logical, TRUE, shows deviance model. show.loglik Logical, TRUE, shows log-Likelihood model. show.obs Logical, TRUE, number observations per model printed table summary. show.reflvl Logical, TRUE, additional row inserted table predictor type factor, indicate reference level related factor. terms Character vector names terms (variables) printed table. terms removed output. NULL, terms printed. Note term names must match names model's coefficients. factors, means variable name suffixed related factor level, category counts one term. E.g. rm.terms = \"t_name [2,3]\" remove terms \"t_name2\" \"t_name3\" (assuming variable t_name categorical least factor levels 2 3). Another example iris-dataset: terms = \"Species\" work, instead use terms = \"Species [versicolor,virginica]\". rm.terms Character vector names indicate terms removed output Counterpart terms. rm.terms = \"t_name\" remove term t_name. Default NULL, .e. terms used. factors, levels removed plot need explicitly indicated square brackets, match model's coefficient names, e.g. rm.terms = \"t_name [2,3]\" remove terms \"t_name2\" \"t_name3\" (assuming variable t_name categorical least factor levels 2 3). order.terms Numeric vector, indicating order coefficients plotted. See examples package-vignette. keep, drop Character containing regular expression pattern describes parameters included (keep) excluded (drop) returned data frame. keep may also named list regular expressions. non-matching parameters removed output. keep one element, merged operator regular expression pattern like : \"(one|two|three)\". See details ?parameters::model_parameters. title String, used table caption. pred.labels Character vector labels predictor variables. NULL, pred.labels used first table column predictors' names. default, auto.label = TRUE data labelled, term_labels called retrieve labels coefficients, used predictor labels. data labelled, format_parameters() used create pretty labels. pred.labels = \"\" auto.label = FALSE, raw variable names used model formula used predictor labels. pred.labels named vector, predictor labels (default, names model's coefficients) matched names pred.labels. ensures labels always match related predictor table, matter way predictors sorted. See 'Examples'. dv.labels Character vector labels dependent variables fitted models. dv.labels = \"\", row names dependent variables omitted table. wrap.labels Numeric, determines many chars value, variable axis labels displayed one line line break inserted. bootstrap Logical, TRUE, returns bootstrapped estimates.. iterations Numeric, number bootstrap iterations (default 1000). seed Numeric, number seed replicate bootstrapped estimates. NULL, uses random seed. robust Deprecated. Please use vcov.fun directly specify estimation variance-covariance matrix. vcov.fun Variance-covariance matrix used compute uncertainty estimates (e.g., robust standard errors). argument accepts covariance matrix, function returns covariance matrix, string identifies function used compute covariance matrix. See model_parameters(). vcov.type Deprecated. type-argument now included vcov.args. vcov.args List arguments passed function identified vcov.fun argument. function typically supplied sandwich clubSandwich packages. Please refer documentation (e.g., ?sandwich::vcovHAC) see list available arguments. string.pred Character vector,used headline predictor column. Default \"Predictors\". string.est Character vector, used column heading coefficients. Default based response scale, e.g. logistic regression models, \"Odds Ratios\" chosen, Poisson models \"Incidence Rate Ratios\" etc. Default specified \"Estimate\". string.std Character vector, used column heading standardized beta coefficients. Default \"std. Beta\". string.ci Character vector, used column heading confidence interval values. Default \"CI\". string.se Character vector, used column heading standard error values. Default \"std. Error\". string.std_se Character vector, used column heading standard error standardized coefficients. Default \"standardized std. Error\". string.std_ci Character vector, used column heading confidence intervals standardized coefficients. Default \"standardized std. Error\". string.p Character vector, used column heading p values. Default \"p\". string.std.p Character vector, used column heading p values. Default \"std. p\". string.df Character vector, used column heading degrees freedom. Default \"df\". string.stat Character vector, used test statistic. Default \"Statistic\". string.std.stat Character vector, used test statistic. Default \"std. Statistic\". string.resp Character vector, used column heading response level multinominal categorical models. Default \"Response\". string.intercept Character vector, used name intercept parameter. Default \"(Intercept)\". strings Named character vector, alternative arguments like string.ci string.p etc. name (lhs) must one string-indicator aforementioned arguments, value (rhs) string used column heading. E.g., strings = c(ci = \"Conf.Int.\", se = \"std. Err\") equivalent setting string.ci = \"Conf.Int.\", string.se = \"std. Err\". ci.hyphen Character vector, indicating hyphen confidence interval range. May HTML entity. See 'Examples'. minus.sign string, indicating minus sign negative numbers. May HTML entity. See 'Examples'. collapse.ci Logical, FALSE, CI values shown separate table column. collapse.se Logical, FALSE, SE values shown separate table column. linebreak Logical, TRUE collapse.ci = FALSE collapse.se = FALSE, inserts line break estimate CI resp. SE values. FALSE, values printed line estimate values. col.order Character vector, indicating columns printed order. Column names excluded col.order shown table output. However, column names included, shown table related argument (like show.est \"estimate\") set TRUE another valid value. Table columns printed order appear col.order. digits Amount decimals estimates digits.p Amount decimals p-values digits.rsq Amount decimals r-squared values digits.re Amount decimals random effects part summary table. emph.p Logical, TRUE, significant p-values shown bold faced. df.method, p.val Method computing degrees freedom p-values, standard errors confidence intervals (CI). applies mixed models. Use df.method = \"wald\" faster, less precise computation. use residual degrees freedom (returned df.residual()) linear mixed models, Inf degrees freedom model families. df.method = \"kenward\" (df.method = \"kr\") uses Kenward-Roger approximation degrees freedom. df.method = \"satterthwaite\" uses Satterthwaite's approximation \"ml1\" uses \"m-l-1\" heuristic see degrees_of_freedom details). Use show.df = TRUE show approximated degrees freedom coefficient. p.style Character, indicating p-values printed numeric value (\"numeric\"), 'stars' (asterisks) (\"stars\"), scientific (\"scientific\"). Scientific numeric style can combined \"stars\", e.g. \"numeric_stars\" p.threshold Numeric vector length 3, indicating treshold annotating p-values asterisks. applies p.style = \"asterisk\". p.adjust Character vector, NULL, indicates method adjust p-values. See p.adjust details. case Desired target case. Labels automatically converted specified character case. See snakecase::to_any_case() details argument. default, case specified, set \"parsed\", unless prefix.labels \"none\". prefix.labels either \"label\" (\"l\") \"varname\" (\"v\") case specified, set NULL - convenient default prefixing labels. auto.label Logical, TRUE (default),  data labelled,  term_labels called retrieve labels  coefficients, used predictor labels. data  labelled, format_parameters()  used create pretty labels. auto.label = FALSE, original variable names value labels (factor levels) used. prefix.labels Indicates whether value labels categorical variables prefixed, e.g. variable name variable label. See argument prefix term_labels details. bpe Stan-models (fitted rstanarm- brms-package), Bayesian point estimate , default, median posterior distribution. Use bpe define functions calculate Bayesian point estimate. bpe needs character naming specific function, passed fun-argument typical_value. , bpe = \"mean\" calculate mean value posterior distribution. CSS list user-defined style-sheet-definitions, according official CSS syntax. See 'Details' package-vignette. file Destination file, output saved file. NULL (default), output saved temporary file opened either IDE's viewer pane default web browser. use.viewer Logical, TRUE, HTML table shown IDE's viewer pane. FALSE viewer available, HTML table opened web browser. encoding Character vector, indicating charset encoding used variable value labels. Default \"UTF-8\". Windows Systems, encoding = \"Windows-1252\" might necessary proper display special characters.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print regression models as HTML table — tab_model","text":"Invisibly returns web page style sheet (page.style), web page content (page.content), complete html-output (page.complete) html-table inline-css use knitr (knitr) use.","code":""},{"path":[]},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_model.html","id":"standardized-estimates","dir":"Reference","previous_headings":"","what":"Standardized Estimates","title":"Print regression models as HTML table — tab_model","text":"Default standardization done completely refitting model   standardized data. Hence, approach equal standardizing   variables fitting model, particularly recommended   complex models include interactions transformations (e.g., polynomial   spline terms). show.std = \"std2\", standardization estimates   follows Gelman's (2008)   suggestion, rescaling estimates dividing two standard deviations   instead just one. Resulting coefficients directly comparable   untransformed binary predictors. backward compatibility reasons,   show.std also may logical value; TRUE, normal standardized   estimates printed (effect show.std = \"std\"). Use   show.std = NULL (default) show.std = FALSE, standardization   required.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_model.html","id":"how-do-i-use-css-argument-","dir":"Reference","previous_headings":"","what":"How do I use CSS-argument?","title":"Print regression models as HTML table — tab_model","text":"CSS-argument, visual appearance tables    can modified. get overview style-sheet-classnames    used function, see return value page.style details.    Arguments list following syntax: class-names \"css.\"-prefix argument name style-definition must end semicolon can add style information default styles using + (plus-sign)    initial character argument attributes. Examples: css.table = 'border:2px solid red;' solid 2-pixel table border red. css.summary = 'font-weight:bold;' bold fontweight summary row. css.lasttablerow = 'border-bottom: 1px dotted blue;' blue dotted border last table row. css.colnames = '+color:green' add green color formatting column names. css.arc = 'color:blue;' blue text color 2nd row. css.caption = '+color:red;' add red font-color default table caption style.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_model.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Print regression models as HTML table — tab_model","text":"HTML tables can either saved file manually opened (use argument file)         can saved temporary files displayed RStudio Viewer pane (working RStudio)         opened default web browser. Displaying resp. opening temporary file         default behaviour (.e. file = NULL).                  Examples shown three vignettes:         Summary Regression Models HTML Table,         Summary Mixed Models HTML Table         Summary Bayesian Models HTML Table.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_pca.html","id":null,"dir":"Reference","previous_headings":"","what":"Summary of principal component analysis as HTML table — tab_pca","title":"Summary of principal component analysis as HTML table — tab_pca","text":"Performes principle component analysis data frame matrix                (varimax oblimin rotation) displays factor solution HTML                table, saves file.  case data frame used                parameter, Cronbach's Alpha value factor scale calculated,                .e. variables highest loading factor taken                reliability test. result alpha value factor dimension.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_pca.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summary of principal component analysis as HTML table — tab_pca","text":"","code":"tab_pca(   data,   rotation = c(\"varimax\", \"quartimax\", \"promax\", \"oblimin\", \"simplimax\", \"cluster\",     \"none\"),   nmbr.fctr = NULL,   fctr.load.tlrn = 0.1,   title = \"Principal Component Analysis\",   var.labels = NULL,   wrap.labels = 40,   show.cronb = TRUE,   show.msa = FALSE,   show.var = FALSE,   alternate.rows = FALSE,   digits = 2,   string.pov = \"Proportion of Variance\",   string.cpov = \"Cumulative Proportion\",   CSS = NULL,   encoding = NULL,   file = NULL,   use.viewer = TRUE,   remove.spaces = TRUE )"},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_pca.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summary of principal component analysis as HTML table — tab_pca","text":"data data frame used compute PCA, prcomp object. rotation Rotation factor loadings. May one \"varimax\", \"quartimax\", \"promax\", \"oblimin\", \"simplimax\", \"cluster\" \"none\". nmbr.fctr Number factors used calculating rotation. default, value NULL amount factors calculated according Kaiser-criteria. fctr.load.tlrn Specifies minimum difference variable needs factor loadings (components) order indicate clear loading just one factor diffusing factors. instance, variable 0.8, 0.82 0.84 factor loading 3 possible factors can clearly assigned just one factor thus removed principal component analysis. default, minimum difference loading values highest 2nd highest factor 0.1 title String, used table caption. var.labels Character vector variable names, used label variables output. wrap.labels Numeric, determines many chars value, variable axis labels displayed one line line break inserted. show.cronb Logical, TRUE (default), cronbach's alpha value factor scale calculated, .e. variables highest loading factor taken reliability test. result alpha value factor dimension. applies data data frame. show.msa Logical, TRUE, shows additional column measure sampling adequacy according dor component. show.var Logical, TRUE, proportions variances component well cumulative variance shown table footer. alternate.rows Logical, TRUE, rows printed alternatig colors (white light grey default). digits Amount decimals estimates string.pov String table row contains proportions variances. default, \"Proportion Variance\" used. string.cpov String table row contains cumulative variances. default, \"Cumulative Proportion\" used. CSS list user-defined style-sheet-definitions, according official CSS syntax. See 'Details' package-vignette. encoding Character vector, indicating charset encoding used variable value labels. Default \"UTF-8\". Windows Systems, encoding = \"Windows-1252\" might necessary proper display special characters. file Destination file, output saved file. NULL (default), output saved temporary file opened either IDE's viewer pane default web browser. use.viewer Logical, TRUE, HTML table shown IDE's viewer pane. FALSE viewer available, HTML table opened web browser. remove.spaces Logical, TRUE, leading spaces removed lines final string contains html-data. Use , want remove parantheses html-tags. html-source may look less pretty, may help exporting html-tables office tools.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_pca.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summary of principal component analysis as HTML table — tab_pca","text":"Invisibly returns web page style sheet (page.style), web page content (page.content), complete html-output (page.complete), html-table inline-css use knitr (knitr), factor.index, .e. column index variable highest factor loading factor removed.items, .e. variables removed outside fctr.load.tlrn's range. use.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_pca.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summary of principal component analysis as HTML table — tab_pca","text":"","code":"if (FALSE) { # \\dontrun{ # Data from the EUROFAMCARE sample dataset library(sjmisc) data(efc)  # recveive first item of COPE-index scale start <- which(colnames(efc) == \"c82cop1\") # recveive last item of COPE-index scale end <- which(colnames(efc) == \"c90cop9\") # auto-detection of labels if (interactive()) {   tab_pca(efc[, start:end]) }} # }"},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_stackfrq.html","id":null,"dir":"Reference","previous_headings":"","what":"Summary of stacked frequencies as HTML table — tab_stackfrq","title":"Summary of stacked frequencies as HTML table — tab_stackfrq","text":"Shows results stacked frequencies (likert scales) HTML table.                function useful several items identical scale/categories                printed table compare distributions (e.g.                plotting scales like SF, Barthel-Index, Quality--Life-scales etc.).","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_stackfrq.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summary of stacked frequencies as HTML table — tab_stackfrq","text":"","code":"tab_stackfrq(   items,   weight.by = NULL,   title = NULL,   var.labels = NULL,   value.labels = NULL,   wrap.labels = 20,   sort.frq = NULL,   alternate.rows = FALSE,   digits = 2,   string.total = \"N\",   string.na = \"NA\",   show.n = FALSE,   show.total = FALSE,   show.na = FALSE,   show.skew = FALSE,   show.kurtosis = FALSE,   digits.stats = 2,   file = NULL,   encoding = NULL,   CSS = NULL,   use.viewer = TRUE,   remove.spaces = TRUE )"},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_stackfrq.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summary of stacked frequencies as HTML table — tab_stackfrq","text":"items Data frame, grouped data frame, column representing one item. weight.Vector weights applied weight cases. Must vector length input vector. Default NULL, weights used. title String, used table caption. var.labels Character vector variable names, used label variables output. value.labels Character vector (list character vectors) value labels supplied variables, used label variable values output. wrap.labels Numeric, determines many chars value, variable axis labels displayed one line line break inserted. sort.frq logical, indicates whether items ordered highest count first last category items. Use \"first.asc\" order ascending lowest count first category, \"first.desc\" order descending lowest count first category, \"last.asc\" order ascending lowest count last category, \"last.desc\" order descending lowest count last category, NULL (default) sorting. alternate.rows Logical, TRUE, rows printed alternatig colors (white light grey default). digits Numeric, amount digits decimal point rounding values. string.total label total N column. string.na label missing column/row. show.n logical, TRUE, adds total number cases group category labels. show.total logical, TRUE, additional column item's total N printed. show.na logical, TRUE, NA's (missing values) added output. show.skew logical, TRUE, additional column item's skewness printed. skewness retrieved describe-function psych-package. show.kurtosis Logical, TRUE, kurtosis item also shown (see kurtosi describe psych-package details. digits.stats amount digits rounding skewness kurtosis valuess. Default 2, .e. skewness kurtosis values 2 digits decimal point. file Destination file, output saved file. NULL (default), output saved temporary file opened either IDE's viewer pane default web browser. encoding Character vector, indicating charset encoding used variable value labels. Default \"UTF-8\". Windows Systems, encoding = \"Windows-1252\" might necessary proper display special characters. CSS list user-defined style-sheet-definitions, according official CSS syntax. See 'Details' package-vignette. use.viewer Logical, TRUE, HTML table shown IDE's viewer pane. FALSE viewer available, HTML table opened web browser. remove.spaces Logical, TRUE, leading spaces removed lines final string contains html-data. Use , want remove parantheses html-tags. html-source may look less pretty, may help exporting html-tables office tools.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_stackfrq.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summary of stacked frequencies as HTML table — tab_stackfrq","text":"Invisibly returns web page style sheet (page.style), web page content (page.content), complete html-output (page.complete) html-table inline-css use knitr (knitr) use.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_stackfrq.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summary of stacked frequencies as HTML table — tab_stackfrq","text":"","code":"# ------------------------------- # random sample # ------------------------------- # prepare data for 4-category likert scale, 5 items likert_4 <- data.frame(   as.factor(sample(1:4, 500, replace = TRUE, prob = c(0.2, 0.3, 0.1, 0.4))),   as.factor(sample(1:4, 500, replace = TRUE, prob = c(0.5, 0.25, 0.15, 0.1))),   as.factor(sample(1:4, 500, replace = TRUE, prob = c(0.25, 0.1, 0.4, 0.25))),   as.factor(sample(1:4, 500, replace = TRUE, prob = c(0.1, 0.4, 0.4, 0.1))),   as.factor(sample(1:4, 500, replace = TRUE, prob = c(0.35, 0.25, 0.15, 0.25))) )  # create labels levels_4 <- c(\"Independent\", \"Slightly dependent\",               \"Dependent\", \"Severely dependent\")  # create item labels items <- c(\"Q1\", \"Q2\", \"Q3\", \"Q4\", \"Q5\")  # plot stacked frequencies of 5 (ordered) item-scales if (FALSE) { # \\dontrun{ if (interactive()) {   tab_stackfrq(likert_4, value.labels = levels_4, var.labels = items)    # -------------------------------   # Data from the EUROFAMCARE sample dataset   #  Auto-detection of labels   # -------------------------------   data(efc)   # recveive first item of COPE-index scale   start <- which(colnames(efc) == \"c82cop1\")   # recveive first item of COPE-index scale   end <- which(colnames(efc) == \"c90cop9\")    tab_stackfrq(efc[, c(start:end)], alternate.rows = TRUE)    tab_stackfrq(efc[, c(start:end)], alternate.rows = TRUE,                show.n = TRUE, show.na = TRUE)    # --------------------------------   # User defined style sheet   # --------------------------------   tab_stackfrq(efc[, c(start:end)], alternate.rows = TRUE,                show.total = TRUE, show.skew = TRUE, show.kurtosis = TRUE,                CSS = list(css.ncol = \"border-left:1px dotted black;\",                           css.summary = \"font-style:italic;\")) } } # }"},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_xtab.html","id":null,"dir":"Reference","previous_headings":"","what":"Summary of contingency tables as HTML table — tab_xtab","title":"Summary of contingency tables as HTML table — tab_xtab","text":"Shows contingency tables HTML file browser viewer pane, saves file.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_xtab.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summary of contingency tables as HTML table — tab_xtab","text":"","code":"tab_xtab(   var.row,   var.col,   weight.by = NULL,   title = NULL,   var.labels = NULL,   value.labels = NULL,   wrap.labels = 20,   show.obs = TRUE,   show.cell.prc = FALSE,   show.row.prc = FALSE,   show.col.prc = FALSE,   show.exp = FALSE,   show.legend = FALSE,   show.na = FALSE,   show.summary = TRUE,   drop.empty = TRUE,   statistics = c(\"auto\", \"cramer\", \"phi\", \"spearman\", \"kendall\", \"pearson\", \"fisher\"),   string.total = \"Total\",   digits = 1,   tdcol.n = \"black\",   tdcol.expected = \"#339999\",   tdcol.cell = \"#993333\",   tdcol.row = \"#333399\",   tdcol.col = \"#339933\",   emph.total = FALSE,   emph.color = \"#f8f8f8\",   prc.sign = \"&nbsp;&#37;\",   hundret = \"100.0\",   CSS = NULL,   encoding = NULL,   file = NULL,   use.viewer = TRUE,   remove.spaces = TRUE,   ... )  sjt.xtab(   var.row,   var.col,   weight.by = NULL,   title = NULL,   var.labels = NULL,   value.labels = NULL,   wrap.labels = 20,   show.obs = TRUE,   show.cell.prc = FALSE,   show.row.prc = FALSE,   show.col.prc = FALSE,   show.exp = FALSE,   show.legend = FALSE,   show.na = FALSE,   show.summary = TRUE,   drop.empty = TRUE,   statistics = c(\"auto\", \"cramer\", \"phi\", \"spearman\", \"kendall\", \"pearson\", \"fisher\"),   string.total = \"Total\",   digits = 1,   tdcol.n = \"black\",   tdcol.expected = \"#339999\",   tdcol.cell = \"#993333\",   tdcol.row = \"#333399\",   tdcol.col = \"#339933\",   emph.total = FALSE,   emph.color = \"#f8f8f8\",   prc.sign = \"&nbsp;&#37;\",   hundret = \"100.0\",   CSS = NULL,   encoding = NULL,   file = NULL,   use.viewer = TRUE,   remove.spaces = TRUE,   ... )"},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_xtab.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summary of contingency tables as HTML table — tab_xtab","text":"var.row Variable displayed table rows. var.col Cariable displayed table columns. weight.Vector weights applied weight cases. Must vector length input vector. Default NULL, weights used. title String, used table caption. var.labels Character vector variable names, used label variables output. value.labels Character vector (list character vectors) value labels supplied variables, used label variable values output. wrap.labels Numeric, determines many chars value, variable axis labels displayed one line line break inserted. show.obs Logical, TRUE, observed values shown show.cell.prc Logical, TRUE, cell percentage values shown show.row.prc Logical, TRUE, row percentage values shown show.col.prc Logical, TRUE, column percentage values shown show.exp Logical, TRUE, expected values also shown show.legend logical, TRUE, depending plot type function, legend added plot. show.na logical, TRUE, NA's (missing values) added output. show.summary Logical, TRUE, summary row chi-squared statistics, degrees freedom Cramer's V Phi coefficient p-value chi-squared statistics. drop.empty Logical, TRUE variable's values labeled, values / factor levels occurrence data omitted output. FALSE, labeled values observations still printed table (frequency 0). statistics Name measure association computed. May one \"auto\", \"cramer\", \"phi\", \"spearman\", \"kendall\", \"pearson\" \"fisher\". See xtab_statistics. string.total Character label total column / row header digits Amount decimals estimates tdcol.n Color highlighting count (observed) values table cells. Default black. tdcol.expected Color highlighting expected values table cells. Default cyan. tdcol.cell Color highlighting cell percentage values table cells. Default red. tdcol.row Color highlighting row percentage values table cells. Default blue. tdcol.col Color highlighting column percentage values table cells. Default green. emph.total Logical, TRUE, total column row emphasized different background color. See emph.color. emph.color Logical, emph.total = TRUE, color value used painting background total column row. Default light grey. prc.sign percentage sign printed table cells, HTML-format. Default \"&nbsp;%\", hence percentage sign non-breaking-space percentage value. hundret Default value indicates 100-percent column-sums (since rounding values may lead non-exact results). Default \"100.0\". CSS list user-defined style-sheet-definitions, according official CSS syntax. See 'Details' package-vignette. encoding String, indicating charset encoding used variable value labels. Default NULL, encoding auto-detected depending platform (e.g., \"UTF-8\" Unix \"Windows-1252\" Windows OS). Change encoding specific chars properly displayed (e.g. German umlauts). file Destination file, output saved file. NULL (default), output saved temporary file opened either IDE's viewer pane default web browser. use.viewer Logical, TRUE, HTML table shown IDE's viewer pane. FALSE viewer available, HTML table opened web browser. remove.spaces Logical, TRUE, leading spaces removed lines final string contains html-data. Use , want remove parantheses html-tags. html-source may look less pretty, may help exporting html-tables office tools. ... arguments, currently passed test statistics functions chisq.test() fisher.test().","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_xtab.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summary of contingency tables as HTML table — tab_xtab","text":"Invisibly returns web page style sheet (page.style), web page content (page.content), complete html-output (page.complete) html-table inline-css use knitr (knitr) use.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/tab_xtab.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summary of contingency tables as HTML table — tab_xtab","text":"","code":"# prepare sample data set data(efc)  # print simple cross table with labels if (FALSE) { # \\dontrun{ if (interactive()) {   tab_xtab(efc$e16sex, efc$e42dep)    # print cross table with manually set   # labels and expected values   tab_xtab(     efc$e16sex,     efc$e42dep,     var.labels = c(\"Elder's gender\", \"Elder's dependency\"),     show.exp = TRUE   )    # print minimal cross table with labels, total col/row highlighted   tab_xtab(efc$e16sex, efc$e42dep, show.cell.prc = FALSE, emph.total = TRUE)    # User defined style sheet   tab_xtab(efc$e16sex, efc$e42dep,            CSS = list(css.table = \"border: 2px solid;\",                       css.tdata = \"border: 1px solid;\",                       css.horline = \"border-bottom: double blue;\"))    # ordinal data, use Kendall's tau   tab_xtab(efc$e42dep, efc$quol_5, statistics = \"kendall\")    # calculate Spearman's rho, with continuity correction   tab_xtab(     efc$e42dep,     efc$quol_5,     statistics = \"spearman\",     exact = FALSE,     continuity = TRUE   ) } } # }"},{"path":"https://strengejacke.github.io/sjPlot/reference/view_df.html","id":null,"dir":"Reference","previous_headings":"","what":"View structure of labelled data frames — view_df","title":"View structure of labelled data frames — view_df","text":"Save (show) content imported SPSS, SAS Stata data file,                similar labelled data.frame, HTML table.                quick overview shows variable ID number, name, label,                type associated value labels. result can                considered \"codeplan\" data frame.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/view_df.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"View structure of labelled data frames — view_df","text":"","code":"view_df(   x,   weight.by = NULL,   alternate.rows = TRUE,   show.id = TRUE,   show.type = FALSE,   show.values = TRUE,   show.string.values = FALSE,   show.labels = TRUE,   show.frq = FALSE,   show.prc = FALSE,   show.wtd.frq = FALSE,   show.wtd.prc = FALSE,   show.na = FALSE,   max.len = 15,   sort.by.name = FALSE,   wrap.labels = 50,   verbose = FALSE,   CSS = NULL,   encoding = NULL,   file = NULL,   use.viewer = TRUE,   remove.spaces = TRUE )"},{"path":"https://strengejacke.github.io/sjPlot/reference/view_df.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"View structure of labelled data frames — view_df","text":"x (labelled) data frame, imported read_spss, read_sas read_stata function, similar labelled data frame (see set_label set_labels). weight.Name variable x indicated vector weights applied weight  observations. Default NULL, weights used. alternate.rows Logical, TRUE, rows printed alternatig colors (white light grey default). show.id Logical, TRUE (default), variable ID shown first column. show.type Logical, TRUE, adds information variable type variable column. show.values Logical, TRUE (default), variable values shown additional column. show.string.values Logical, TRUE, elements character vectors also shown. default, omitted due possibly overlengthy tables. show.labels Logical, TRUE (default), value labels shown additional column. show.frq Logical, TRUE, additional column frequencies variable shown. show.prc Logical, TRUE, additional column percentage frequencies variable shown. show.wtd.frq Logical, TRUE, additional column weighted frequencies variable shown. Weights strem weight.. show.wtd.prc Logical, TRUE, additional column weighted percentage frequencies variable shown. Weights strem weight.. show.na logical, TRUE, NA's (missing values) added output. max.len Numeric, indicates many values value labels per variable shown. Useful variables many different values, output can truncated. sort..name Logical, TRUE, rows sorted according variable names. default, rows (variables) ordered according order data frame. wrap.labels Numeric, determines many chars value, variable axis labels displayed one line line break inserted. verbose Logical, TRUE, progress bar displayed creating output. CSS list user-defined style-sheet-definitions, according official CSS syntax. See 'Details' package-vignette. encoding Character vector, indicating charset encoding used variable value labels. Default \"UTF-8\". Windows Systems, encoding = \"Windows-1252\" might necessary proper display special characters. file Destination file, output saved file. NULL (default), output saved temporary file opened either IDE's viewer pane default web browser. use.viewer Logical, TRUE, HTML table shown IDE's viewer pane. FALSE viewer available, HTML table opened web browser. remove.spaces Logical, TRUE, leading spaces removed lines final string contains html-data. Use , want remove parantheses html-tags. html-source may look less pretty, may help exporting html-tables office tools.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/view_df.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"View structure of labelled data frames — view_df","text":"Invisibly returns web page style sheet (page.style), web page content (page.content), complete html-output (page.complete) html-table inline-css use knitr (knitr) use.","code":""},{"path":"https://strengejacke.github.io/sjPlot/reference/view_df.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"View structure of labelled data frames — view_df","text":"","code":"if (FALSE) { # \\dontrun{ # init dataset data(efc)  # view variables view_df(efc)  # view variables w/o values and value labels view_df(efc, show.values = FALSE, show.labels = FALSE)  # view variables including variable typed, orderd by name view_df(efc, sort.by.name = TRUE, show.type = TRUE)  # User defined style sheet view_df(efc,         CSS = list(css.table = \"border: 2px solid;\",                    css.tdata = \"border: 1px solid;\",                    css.arc = \"color:blue;\"))} # }"},{"path":[]},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"bug-fixes-2-8-17","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"sjPlot 2.8.17","text":"Fixed issues renamed arguments upstream package ggeffects. Several minor bug fixes.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"sjplot-2815","dir":"Changelog","previous_headings":"","what":"sjPlot 2.8.15","title":"sjPlot 2.8.15","text":"CRAN release: 2023-08-17 Minor fix vignettes.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"sjplot-2814","dir":"Changelog","previous_headings":"","what":"sjPlot 2.8.14","title":"sjPlot 2.8.14","text":"CRAN release: 2023-04-02 tab_model(), plot_model() plot_models() get std.response argument, include exclude response variable standardization.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"bug-fixes-2-8-14","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"sjPlot 2.8.14","text":"Fixed test issues internet connection avaiable. Minor fixes improvements.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"sjplot-2813","dir":"Changelog","previous_headings":"","what":"sjPlot 2.8.13","title":"sjPlot 2.8.13","text":"CRAN release: 2023-03-13","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"bug-fixes-2-8-13","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"sjPlot 2.8.13","text":"Fixed issue knit_print() generics.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"sjplot-2812","dir":"Changelog","previous_headings":"","what":"sjPlot 2.8.12","title":"sjPlot 2.8.12","text":"CRAN release: 2022-11-19","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"general-2-8-12","dir":"Changelog","previous_headings":"","what":"General","title":"sjPlot 2.8.12","text":"Minor changes work properly updates upstream packages various smaller bug fixes improvements.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"sjplot-2811","dir":"Changelog","previous_headings":"","what":"sjPlot 2.8.11","title":"sjPlot 2.8.11","text":"CRAN release: 2022-08-07","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"general-2-8-11","dir":"Changelog","previous_headings":"","what":"General","title":"sjPlot 2.8.11","text":"Minor changes work properly updates upstream packages various smaller bug fixes improvements.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"sjplot-2810","dir":"Changelog","previous_headings":"","what":"sjPlot 2.8.10","title":"sjPlot 2.8.10","text":"CRAN release: 2021-11-26","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"general-2-8-10","dir":"Changelog","previous_headings":"","what":"General","title":"sjPlot 2.8.10","text":"Minor changes work properly updates downstream packages various smaller bug fixes improvements.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"sjplot-289","dir":"Changelog","previous_headings":"","what":"sjPlot 2.8.9","title":"sjPlot 2.8.9","text":"CRAN release: 2021-07-10","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"bug-fixes-2-8-9","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"sjPlot 2.8.9","text":"Fixed issues CRAN checks.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"sjplot-288","dir":"Changelog","previous_headings":"","what":"sjPlot 2.8.8","title":"sjPlot 2.8.8","text":"CRAN release: 2021-05-25","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"changes-to-functions-2-8-8","dir":"Changelog","previous_headings":"","what":"Changes to functions","title":"sjPlot 2.8.8","text":"tab_model() now works properly forthcoming parameters update.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"bug-fixes-2-8-8","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"sjPlot 2.8.8","text":"plot_models() work properly Bayesian models.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"sjplot-287","dir":"Changelog","previous_headings":"","what":"sjPlot 2.8.7","title":"sjPlot 2.8.7","text":"CRAN release: 2021-01-10","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"changes-to-functions-2-8-7","dir":"Changelog","previous_headings":"","what":"Changes to functions","title":"sjPlot 2.8.7","text":"tab_model() also gains encoding argument. tab_df() tab_dfs() longer set argument show.rownames TRUE. Therefore, functions now use row numbers row names, rownames present. tab_dfs() also gains digits argument.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"bug-fixes-2-8-7","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"sjPlot 2.8.7","text":"Argument df.method tab_model() accept available options documented. Fix CRAN check issues (warnings new R-devel).","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"sjplot-286","dir":"Changelog","previous_headings":"","what":"sjPlot 2.8.6","title":"sjPlot 2.8.6","text":"CRAN release: 2020-10-28","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"changes-to-functions-2-8-6","dir":"Changelog","previous_headings":"","what":"Changes to functions","title":"sjPlot 2.8.6","text":"dv.labels = \"\" tab_model(), row names dependent variables omitted.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"bug-fixes-2-8-6","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"sjPlot 2.8.6","text":"Fix CRAN check issues (removed cross-references archived packages). minus.sign argument tab_model() now works. show.std = TRUE tab_model() exponentiate standardized coefficients non-Gaussian models.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"sjplot-285","dir":"Changelog","previous_headings":"","what":"sjPlot 2.8.5","title":"sjPlot 2.8.5","text":"CRAN release: 2020-09-24","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"changes-to-functions-2-8-5","dir":"Changelog","previous_headings":"","what":"Changes to functions","title":"sjPlot 2.8.5","text":"tab_model() gains argument df.method, replace less generic p.val argument future. Currently, df.method alias p.val.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"bug-fixes-2-8-5","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"sjPlot 2.8.5","text":"Fixed issue wrong n’s plot_stackfrq() weights applied. Fixed issue plot_stackfrq() weights applied items sorted. Fixed issue plot_models() models without intercept. Fixed issue wrong legend labelling plot_models() showing p-stars. Fixed issue plot_model() type = \"int\" detecting interaction terms partly parenthesis (like * (b + c)). Fixed issue tab_model() arguments show.stat = TRUE show.std = TRUE, related statistic CI columns standardized coefficients shown. Fixed issue tab_model() brmsfit models longer show random effects information last update performance package. Fixed issue argument show.rownames tab_df().","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"sjplot-284","dir":"Changelog","previous_headings":"","what":"sjPlot 2.8.4","title":"sjPlot 2.8.4","text":"CRAN release: 2020-05-24","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"changes-to-functions-2-8-4","dir":"Changelog","previous_headings":"","what":"Changes to functions","title":"sjPlot 2.8.4","text":"robust estimation (argument vcov.fun tab_model() plot_model()) now also uses thus accepts estimation-types package clubSandwich. tab_model() now accepts options p.val supported parameters::model_parameters(). p.style argument tab_model() slightly revised, now also accepts \"scientific\" option scientific notation p-values. tab_model() gets digits.re argument define decimal part random effects summary. plot_models() gains value.size line.size arguments, similar plot_model(). plot_models() sort coefficients natural order now.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"bug-fixes-2-8-4","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"sjPlot 2.8.4","text":"Fixed bug plot_xtab() wrong order legend labels. Fixed bug plot_models() wrong axis title exponentiated coefficients. Fixed bug tab_model() show standard error standardized coefficients show.se = TRUE.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"sjplot-283","dir":"Changelog","previous_headings":"","what":"sjPlot 2.8.3","title":"sjPlot 2.8.3","text":"CRAN release: 2020-03-09","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"general-2-8-3","dir":"Changelog","previous_headings":"","what":"General","title":"sjPlot 2.8.3","text":"tab_model() plot_model() now support clogit models (requires latest update package insight).","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"changes-to-functions-2-8-3","dir":"Changelog","previous_headings":"","what":"Changes to functions","title":"sjPlot 2.8.3","text":"tab_model() gets p.adjust argument adjust p-values multiple comparisons. tab_model(), plot_model() plot_models() get robust-argument easily compute standard errors, confidence intervals p-values based robust estimation variance-covariance matrix. robust just convenient shortcut vcov.fun vcov.type.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"bug-fixes-2-8-3","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"sjPlot 2.8.3","text":"Fixed issue tab_model() plot_model() certain cases coefficients estimated NA. Fixed issue tab_model() collapse.ci Bayesian models. Fixed issue tab_model() p.val=\"kr\" show.df=TRUE. Fixed issue tab_model() formatting issues p-values standardized coefficients requested. Fixed issue tab_model() due changes packages sjPlot depends .","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"sjplot-282","dir":"Changelog","previous_headings":"","what":"sjPlot 2.8.2","title":"sjPlot 2.8.2","text":"CRAN release: 2020-01-23","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"function-renaming-2-8-2","dir":"Changelog","previous_headings":"","what":"Function renaming","title":"sjPlot 2.8.2","text":"sjt.itemanalysis() now named tab_itemscale(). sjt.xtab() now named tab_xtab().","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"changes-to-functions-2-8-2","dir":"Changelog","previous_headings":"","what":"Changes to functions","title":"sjPlot 2.8.2","text":"Improved handling tab_model() robust estimation general Kenward-Roger Satterthwaite approximations particular linear mixed models. Revised code cope forthcoming tidyselect-update.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"bug-fixes-2-8-2","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"sjPlot 2.8.2","text":"Improved tab_df() now uses value labels factors instead numeric values. Fixed issues related lates brms-update.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"sjplot-281","dir":"Changelog","previous_headings":"","what":"sjPlot 2.8.1","title":"sjPlot 2.8.1","text":"CRAN release: 2019-12-03","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"changes-to-functions-2-8-1","dir":"Changelog","previous_headings":"","what":"Changes to functions","title":"sjPlot 2.8.1","text":"tab_model() gets arguments bootstrap, iterations seed return bootstrapped estimates.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"bug-fixes-2-8-1","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"sjPlot 2.8.1","text":"Fixed issue tab_model() detecting labels auto.label = TRUE. Fixed issue tab_model() negative binomial hurdle mixed models (.e. glmmTMB models truncated negative-binomial family). Fixed bug tab_model() show.reflvl = TRUE. Fixed bug tab_model() labels coefficients matching correct coefficients.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"sjplot-280","dir":"Changelog","previous_headings":"","what":"sjPlot 2.8.0","title":"sjPlot 2.8.0","text":"CRAN release: 2019-11-18","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"breaking-changes-2-8-0","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"sjPlot 2.8.0","text":"Cluster functions removed, now re-implemented parameters package.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"general-2-8-0","dir":"Changelog","previous_headings":"","what":"General","title":"sjPlot 2.8.0","text":"Standardization model parameters (plot_model() tab_model()) now uses standardization based refitting model.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"changes-to-functions-2-8-0","dir":"Changelog","previous_headings":"","what":"Changes to functions","title":"sjPlot 2.8.0","text":"plot_model() gets type = \"emm\" marginal effects plot type, similar type = \"eff\". See Plotting Marginal Effects Regression Models details. verbose-argument view_df() now defaults FALSE. Updated re-arranged internal color palette, especially better behaviour selecting colors continuous palettes (see show_pals()).","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"bug-fixes-2-8-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"sjPlot 2.8.0","text":"sort.est = NULL plot_model() now preserves original order coefficients. Fixed bug automatic axis labelling plot_frq() non-labelled, numeric values. Fixed bug plot_frq() plotting factors. Arguments string.std_ci string.std_se longer ignored tab_model().","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"sjplot-272","dir":"Changelog","previous_headings":"","what":"sjPlot 2.7.2","title":"sjPlot 2.7.2","text":"CRAN release: 2019-09-29","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"general-2-7-2","dir":"Changelog","previous_headings":"","what":"General","title":"sjPlot 2.7.2","text":"Replaced performance::principal_component() parameters::principal_component(). Fixed CRAN check issues, due latest bayestestR update.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"function-renaming-2-7-2","dir":"Changelog","previous_headings":"","what":"Function renaming","title":"sjPlot 2.7.2","text":"sjp.grpfrq() now names plot_grpfrq(). sjp.xtab() now names plot_xtab().","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"changes-to-functions-2-7-2","dir":"Changelog","previous_headings":"","what":"Changes to functions","title":"sjPlot 2.7.2","text":"plot_grid() gets tags-argument add tags plot-panels.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"bug-fixes-2-7-2","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"sjPlot 2.7.2","text":"Fixed bug plot_stackfrq() data frames many missing values. Fixed bug sorting frequencies plot_frq() vector labels values. Fixed bug tab_model() show.reflvl = TRUE insert reference category first place, alphabetical order.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"sjplot-271","dir":"Changelog","previous_headings":"","what":"sjPlot 2.7.1","title":"sjPlot 2.7.1","text":"CRAN release: 2019-09-07","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"general-2-7-1","dir":"Changelog","previous_headings":"","what":"General","title":"sjPlot 2.7.1","text":"Minor revisions meet changes forthcoming update tidyr. new color palettes added (see show_sjplot_pals()).","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"changes-to-functions-2-7-1","dir":"Changelog","previous_headings":"","what":"Changes to functions","title":"sjPlot 2.7.1","text":"tab_model() now supports gamlss models. tab_df() gets digits argument, round numeric values output.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"bug-fixes-2-7-1","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"sjPlot 2.7.1","text":"Fixed bug tab_model() show.df = TRUE lmerModLmerTest. Fixed bug tab_stackfrq() items different amount valid values.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"sjplot-270","dir":"Changelog","previous_headings":"","what":"sjPlot 2.7.0","title":"sjPlot 2.7.0","text":"CRAN release: 2019-08-02","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"renamed-functions-2-7-0","dir":"Changelog","previous_headings":"","what":"Renamed functions","title":"sjPlot 2.7.0","text":"sjp.stackfrq() renamed plot_stackfrq(). sjt.stackfrq() renamed tab_stackfrq().","code":""},{"path":[]},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"plot_likert-2-7-0","dir":"Changelog","previous_headings":"Changes to functions","what":"plot_likert()","title":"sjPlot 2.7.0","text":"showed category labels top bottom legends two rows six categories. Also, categories ordered column wise instead row wise. behaviour can now controlled grouped likert plots, using group.legend.options. ordering now defaults row wise user can force categories onto single row. now automatically adjusts labels avoid overlapping.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"tab_model-2-7-0","dir":"Changelog","previous_headings":"Changes to functions","what":"tab_model()","title":"sjPlot 2.7.0","text":"now supports wbm()-models panelr-package. gets show.aicc-argument show second order AIC. gets show.reflvl-argument show reference level factors. gets string.std_se string.std_ci-argument change column header standard errors confidence intervals standardized coefficients. longer prints message default p-values mixed models based Wald approximation. show.ci50 defaults FALSE now.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"sjtitemanalysis-2-7-0","dir":"Changelog","previous_headings":"Changes to functions","what":"sjt.itemanalysis()","title":"sjPlot 2.7.0","text":"sjt.itemanalysis() now works ordered factors. clearer error message added unordered factors used. old error message helpful. factor.groups argument can now \"auto\" detect factor groups based pca Varimax rotation.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"sjpstackrq-2-7-0","dir":"Changelog","previous_headings":"Changes to functions","what":"sjp.stackrq()","title":"sjPlot 2.7.0","text":"sjp.stackfrq() renamed plot_stackfrq(). sjp.stackfrq() (now named: plot_stackfrq()) gets show.n-argument also show count values. option can combined show.prc. sjp.stackfrq() (now named: plot_stackfrq()) now also works grouped data frames.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"changes-to-other-functions-2-7-0","dir":"Changelog","previous_headings":"Changes to functions","what":"changes to other functions","title":"sjPlot 2.7.0","text":"plot_model() now supports wbm()-models panelr-package.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"bug-fixes-2-7-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"sjPlot 2.7.0","text":"plot_model(type = \"int\") now also recognized interaction terms : formula. Argument string.est tab_model() overwrite default label estimate-column-header. Minor fix tab_model() mixed models can’t compute R2. Fix issue tab_model() printing robust standard errors CI (.e. using arguments vcov*). plot_likert() option reverse.scale = TRUE resulted values = \"sum.inside\" outside way around. fixed now. view_df() mixed labels frequency values value labels present, values data. Argument wrap.labels plot_frq() properly work factor levels. Fix issue plot_models() stopped models. Fix issue sjt.stackfrq(), show.na = TRUE items zero-values.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"sjplot-263","dir":"Changelog","previous_headings":"","what":"sjPlot 2.6.3","title":"sjPlot 2.6.3","text":"CRAN release: 2019-04-27","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"general-2-6-3","dir":"Changelog","previous_headings":"","what":"General","title":"sjPlot 2.6.3","text":"Export dplyr::n(), meet changes dplyr 0.8.0. plot_model() tab_model() now support MixMod-objects package GLMMadpative, mlogit- gmnl-models.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"renamed-functions-2-6-3","dir":"Changelog","previous_headings":"","what":"Renamed functions","title":"sjPlot 2.6.3","text":"sjp.kfold_cv() renamed plot_kfold_cv(). sjp.frq() renamed plot_frq().","code":""},{"path":[]},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"tab_model-2-6-3","dir":"Changelog","previous_headings":"Changes to functions","what":"tab_model()","title":"sjPlot 2.6.3","text":"tab_model() gets show.ngrps-argument, adds back functionality print number random effects groups mixed models. tab_model() gets show.loglik-argument, adds back functionality print model’s log-Likelihood. tab_model() gets strings-argument, convenient shortcut setting column-header strings. tab_model() gets additional arguments vcov.fun, vcov.type vcov.args passed sjstats::robust(), calculate different types (clustered) robust standard errors. p.style-argument now also allows printing numeric p-values asterisks, using p.style = \"\".","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"plot_likert-2-6-3","dir":"Changelog","previous_headings":"Changes to functions","what":"plot_likert()","title":"sjPlot 2.6.3","text":"plot_likert() gets reverse.scale argument reverse order categories, positive negative values switch position. plot_likert() gets groups argument, group items plot (thanks @ndevln). Argument grid.range plot_likert() now may also vector length 2, define diffent length left right x-axis scales.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"other-2-6-3","dir":"Changelog","previous_headings":"Changes to functions","what":"Other","title":"sjPlot 2.6.3","text":"plot_frq() (former sjp.frq()) now pipe-consistent syntax, enables plotting multiple variables one function call supports grouped data frames. plot_model() gets additional arguments vcov.fun, vcov.type vcov.args passed sjstats::robust(), calculate different types (clustered) robust standard errors. sjt.xtab(), sjp.xtab(), plot_frq() sjp.grpfrq() get drop.empty()-argument, drop values / factor levels observations output.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"bug-fixes-2-6-3","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"sjPlot 2.6.3","text":"Legend labels inverted brms-models plot_model(..., type = \"diag\"). Legend labels duplicated marginal effects plots color =\"bw\" legend.title specified. Fixed encoding issues help-files. view_df() truncate frequency- percentage-values variables value labels truncated certain maximum number. tab_model() print number observations coxph-models.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"sjplot-262","dir":"Changelog","previous_headings":"","what":"sjPlot 2.6.2","title":"sjPlot 2.6.2","text":"CRAN release: 2018-12-18","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"general-2-6-2","dir":"Changelog","previous_headings":"","what":"General","title":"sjPlot 2.6.2","text":"Revised help-files vignettes.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"removed--defunct-2-6-2","dir":"Changelog","previous_headings":"","what":"Removed / Defunct","title":"sjPlot 2.6.2","text":"Following functions now defunct: sjt.lm(), sjt.glm(), sjt.lmer() sjt.glmer(). Please use tab_model() instead.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"changes-to-functions-2-6-2","dir":"Changelog","previous_headings":"","what":"Changes to functions","title":"sjPlot 2.6.2","text":"tab_model() supports printing simplex parameters monotonic effects brms models. tab_model() gets prefix.labels-argument add prefix labels categorical terms. rotation-argument sjt.pca() sjp.pca() now supports rotations psych::principal().","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"bug-fixes-2-6-2","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"sjPlot 2.6.2","text":"plot_model() longer automatically changes plot-type \"slope\" models one predictor categorical two levels. type = \"eff\" type = \"pred\" plot_model() work terms specified. robust standard errors requested tab_model(), confidence intervals p-values now re-calculated adjusted based robust standard errors. colors = \"bw\" recognized correctly plot_model(..., type = \"int\"). Fix issue sjp.frq() correct axis labels non-labelled character vectors.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"sjplot-261","dir":"Changelog","previous_headings":"","what":"sjPlot 2.6.1","title":"sjPlot 2.6.1","text":"CRAN release: 2018-10-14","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"general-2-6-1","dir":"Changelog","previous_headings":"","what":"General","title":"sjPlot 2.6.1","text":"Removed defunct functions.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"deprecated-2-6-1","dir":"Changelog","previous_headings":"","what":"Deprecated","title":"sjPlot 2.6.1","text":"sjt.lm(), sjt.glm(), sjt.lmer() sjt.glmer() now deprecated. Please use tab_model() instead.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"changes-to-functions-2-6-1","dir":"Changelog","previous_headings":"","what":"Changes to functions","title":"sjPlot 2.6.1","text":"Arguments dot.size line.size plot_model() now also apply marginal effects diagnostic plots. plot_model() now uses free x-axis scale facets models zero-inflated part. plot_model() now shows multiple plots models zero-inflated parts grids = FALSE. tab_model() gets p.style p.threshold argument indicate significance levels asteriks, determine threshold estimate considered significant. plot_model() plot_models() get p.threshold argument determine threshold estimate considered significant.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"bug-fixes-2-6-1","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"sjPlot 2.6.1","text":"Fixed bug last update made value labels disappear plot_likert(). tab_model() now also accepts multiple model-objects stored list argument, stated help-file. file-argument now works sjt.itemanalysis(). Argument show.ci tab_model() compute confidence intervals different levels.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"sjplot-260","dir":"Changelog","previous_headings":"","what":"sjPlot 2.6.0","title":"sjPlot 2.6.0","text":"CRAN release: 2018-08-23","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"general-2-6-0","dir":"Changelog","previous_headings":"","what":"General","title":"sjPlot 2.6.0","text":"sjp.scatter() revised renamed plot_scatter(). plot_scatter() pipe-friendly, also works grouped data frames. sjp.gpt() revised renamed plot_gpt(). plot_gpt() pipe-friendly, also works grouped data frames. Reduce package dependencies.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"renamed-functions-2-6-0","dir":"Changelog","previous_headings":"","what":"Renamed functions","title":"sjPlot 2.6.0","text":"sjp.scatter() renamed plot_scatter(). sjp.likert() renamed plot_likert(). sjp.gpt() renamed plot_gpt(). sjp.resid() renamed plot_residuals().","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"changes-to-functions-2-6-0","dir":"Changelog","previous_headings":"","what":"Changes to functions","title":"sjPlot 2.6.0","text":"Improved support brmsfit-objects categorical-family plot_model() tab_model(). tab_model() gets show.adj.icc-argument, also show adjusted ICC mixed models. tab_model() gets col.order-argument, reorder table columns. Argument hide.progress view_df() deprecated. Please use verbose now. statistics-argument sjt.xtab() gets \"fisher\"-option, force Fisher’s Exact Test used.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"removed--defunct-2-6-0","dir":"Changelog","previous_headings":"","what":"Removed / Defunct","title":"sjPlot 2.6.0","text":"Following functions now defunct: sjp.lm(), sjp.glm(), sjp.lmer(), sjp.glmer() sjp.int(). Please use plot_model() instead. sjt.frq(). Please use sjmisc::frq(= \"v\") instead.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"bug-fixes-2-6-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"sjPlot 2.6.0","text":"Due changes broom lmerTest packages, tidiers longer work lmerModLmerTest objects. Fix issue standardized coefficient (argument show.std) tab_model().","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"sjplot-250","dir":"Changelog","previous_headings":"","what":"sjPlot 2.5.0","title":"sjPlot 2.5.0","text":"CRAN release: 2018-07-12","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"new-functions-2-5-0","dir":"Changelog","previous_headings":"","what":"New functions","title":"sjPlot 2.5.0","text":"tab_model() replacement sjt.lm(), sjt.glm(), sjt.lmer() sjt.glmer(). Furthermore, tab_model() designed work model-objects plot_model(). New colour scales ggplot-objects: scale_fill_sjplot() scale_color_sjplot(). provide predifined colour palettes package. show_sjplot_pals() show predefined colour palettes provided package. sjplot_pal() return colour values specific palette.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"deprecated-2-5-0","dir":"Changelog","previous_headings":"","what":"Deprecated","title":"sjPlot 2.5.0","text":"Following functions now deprecated: sjp.lm(), sjp.glm(), sjp.lmer(), sjp.glmer() sjp.int(). Please use plot_model() instead. sjt.frq(). Please use sjmisc::frq(= \"v\") instead.","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"removed--defunct-2-5-0","dir":"Changelog","previous_headings":"","what":"Removed / Defunct","title":"sjPlot 2.5.0","text":"Following functions now defunct: sjt.grpmean(), sjt.mwu() sjt.df(). replacements sjstats::grpmean(), sjstats::mwu() tab_df() resp. tab_dfs().","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"changes-to-functions-2-5-0","dir":"Changelog","previous_headings":"","what":"Changes to functions","title":"sjPlot 2.5.0","text":"plot_model() plot_models() get prefix.labels-argument, prefix automatically retrieved term labels either related variable name label. plot_model() gets show.zeroinf-argument show hide zero-inflation-part models plot. plot_model() gets jitter-argument add random variation data points plot types accept show.data = TRUE. plot_model() gets legend.title-argument define legend title plots display legend. plot_model() now passes arguments ... ggeffects::plot() marginal effects plots. plot_model() now plots zero-inflated part model brmsfit-objects. plot_model() now plots multivariate response models, .e. models multiple outcomes. Diagnostic plots plot_model() (type = \"diag\") can now also used brmsfit-objects. Axis limits diagnostic plots plot_model() (type = \"diag\") Stan-models (brmsfit stanreg resp. stanfit) can now set axis.lim-argument. grid.breaks-argument plot_model() plot_models() now also takes vector values directly define grid breaks plot. Better default calculation grid breaks plot_model() plot_models() grid.breaks-argument length one. terms-argument plot_model() now also allows specification range numeric values square brackets marginal effects plots, e.g. terms = \"age [30:50]\" terms = \"age [pretty]\". coefficient-plots, terms- rm.terms-arguments plot_model() now also allows specification factor levels categorical terms. Coefficients indicted factor levels kept resp. removed (see ?plot_model details). plot_model() now supports clmm-objects (package ordinal). plot_model(type = \"diag\") now also shows random-effects QQ-plots glmmTMB-models, also plots random-effects QQ-plots random effects (model one random effect term).","code":""},{"path":"https://strengejacke.github.io/sjPlot/news/index.html","id":"bug-fixes-2-5-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"sjPlot 2.5.0","text":"plot_model(type = \"re\") now supports standard errors confidence intervals glmmTMB-objects. Fixed typo glmmTMB-tidier, may returned wrong data zero-inflation part model. Multiple random intercepts multilevel models fitted brms area now shown facet per intercept. Remove unnecessary warning sjp.likert() uneven category count neutral category specified. plot_model(type = \"int\") automatically select mdrt.values properly non-integer variables. sjp.grpfrq() now correctly uses complete space facets facet.grid = TRUE. sjp.grpfrq(type = \"boxplot\") correctly label x-axis one category elements vector. Problems German umlauts printing HTML tables fixed.","code":""}]