Improve emmeans doc with pairs / new release (another CRAN submission…

… round) (#373)
openpharma · Nov 17, 2023 · 4da1cd1 · 4da1cd1
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diff --git a/DESCRIPTION b/DESCRIPTION
@@ -1,7 +1,7 @@
 Type: Package
 Package: mmrm
 Title: Mixed Models for Repeated Measures
-Version: 0.3.5.9003
+Version: 0.3.6
 Authors@R: c(
     person("Daniel", "Sabanes Bove", , "[email protected]", role = c("aut", "cre")),
     person("Julia", "Dedic", , "[email protected]", role = "aut"),

diff --git a/NEWS.md b/NEWS.md
@@ -1,4 +1,4 @@
-# mmrm 0.3.5.9003
+# mmrm 0.3.6
 
 ### New Features
 

diff --git a/R/interop-emmeans.R b/R/interop-emmeans.R
@@ -5,6 +5,9 @@
 #' This package includes methods that allow `mmrm` objects to be used
 #' with the `emmeans` package. `emmeans` computes estimated marginal means
 #' (also called least-square means) for the coefficients of the MMRM.
+#' We can also e.g. obtain differences between groups by applying
+#' [`pairs()`][emmeans::pairs.emmGrid()] on the object returned
+#' by [emmeans::emmeans()].
 #'
 #' @examples
 #' fit <- mmrm(
@@ -13,6 +16,7 @@
 #' )
 #' if (require(emmeans)) {
 #'   emmeans(fit, ~ ARMCD | AVISIT)
+#'   pairs(emmeans(fit, ~ ARMCD | AVISIT), reverse = TRUE)
 #' }
 #' @name emmeans_support
 NULL

diff --git a/man/emmeans_support.Rd b/man/emmeans_support.Rd
diff --git a/vignettes/subsections/_intro-hypothesis_testing.Rmd b/vignettes/subsections/_intro-hypothesis_testing.Rmd
@@ -72,18 +72,24 @@ course together with multidimensional contrasts.
 
 This package includes methods that allow `mmrm` objects to be used with the
 `emmeans` package. `emmeans` computes estimated marginal means (also called
-least-square means) for the coefficients of the MMRM.
+least-square means) for the coefficients of the MMRM. For example, in order
+to see the least-square means by visit and by treatment arm:
 
 ```{r emmeans}
-fit <- mmrm(
-  formula = FEV1 ~ RACE + ARMCD * AVISIT + us(AVISIT | USUBJID),
-  data = fev_data
-)
-
-if (require(emmeans)) {
-  emmeans(fit, ~ ARMCD | AVISIT)
-}
+library(emmeans)
+lsmeans_by_visit <- emmeans(fit, ~ ARMCD | AVISIT)
+lsmeans_by_visit
 ```
 
 Note that the degrees of freedom choice is inherited here from the initial `mmrm`
 fit.
+Furthermore, we can also obtain the differences between the treatment arms for each visit
+by applying `pairs()` on the object returned by `emmeans()` earlier:
+
+```{r pdiff}
+pairs(lsmeans_by_visit, reverse = TRUE)
+```
+
+(This is similar like the `pdiff` option in SAS `PROC MIXED`.)
+Note that we use here the `reverse` argument to obtain treatment minus
+placebo results, instead of placebo minus treatment results.