updated generalized linear model

20-linear_mixed_effects_models4.Rmd

@@ -230,7 +230,9 @@ ggplot(data = df.politeness,
 We fit the model and compute the contrasts. 
 
 ```{r, message=FALSE}
-fit = lmer(formula = frequency ~ 1 + attitude * gender + (1 + attitude | subject) + (1 | scenario),
+fit = lmer(formula = frequency ~ 1 + attitude * gender + 
+             (1 + attitude | subject) + 
+             (1 + attitude | scenario),
            data = df.politeness)
 
 fit %>% 
@@ -752,7 +754,8 @@ sd_residual = 1
 
 df.data = tibble(participant = rep(1:n_participants, each = n_timepoints),
                  timepoint = rep(1:n_timepoints, times = n_participants),
-                 intercept_participant = rep(rnorm(n_participants, sd = sd_intercept_participant), 
+                 intercept_participant = rep(rnorm(n_participants,
+                                                   sd = sd_intercept_participant), 
                                              each = n_timepoints),
                  time_participant = rep(rnorm(n_participants, sd = sd_time_participant), 
                                              each = n_timepoints)) %>% 
@@ -807,7 +810,6 @@ fit %>%
   summary()
 ```
 
-
 ## Bootstrapping
 
 Bootstrapping is a good way to estimate our uncertainty on the parameter estimates in the model. 
@@ -884,6 +886,7 @@ boot.ci(boot.lmer,
         index = 2,
         type = "perc")
 ```
+
 Let's plot the distribution of estimates.
 
 ```{r}

21-generalized_linear_model.Rmd

@@ -22,7 +22,7 @@ library("janitor")    # for cleaning column names
 library("broom")      # for tidying up linear models 
 library("lme4")       # for linear mixed effects models
 library("boot")       # for bootstrapping (also has an inverse logit function)
-library("ggeffects")  # for showing effects in linear, generalized linear, and other models
+library("ggeffects")  # for computing marginal effects 
 library("afex")       # for significance testing of mixed effects models 
 library("emmeans")    # for the joint_tests() function
 library("tidyverse")  # for wrangling, plotting, etc. 
@@ -442,7 +442,9 @@ b1 = 1
 df.data = tibble(x = rnorm(n = sample_size),
                  y = b0 + b1 * x,
                  p = inv.logit(y)) %>% 
-  mutate(response = rbinom(n(), size = 1, p = p))
+  mutate(response = rbinom(n(),
+                           size = 1,
+                           p = p))
 
 # fit model 
 fit = glm(formula = response ~ 1 + x,
@@ -529,7 +531,9 @@ fit.augmented = glm(formula = survived ~ 1 + sex + fare,
                     data = df.titanic)
 
 # likelihood ratio test
-anova(fit.compact, fit.augmented, test = "LRT")
+anova(fit.compact,
+      fit.augmented,
+      test = "LRT")
 ```
 
 Note that in order to get a p-value out of this, we need to specify what statistical test we'd like to run. In this case, we use the likelihood ratio test ("LRT").
@@ -620,8 +624,6 @@ fit_c =  glmer(repeatgr ~ 1 +  minority + (1 | school_nr),
 anova(fit_a, fit_c, test = "LRT")
 ```
 
-
-
 ## Additional information
 
 ### Misc 

docs/404.html

@@ -23,7 +23,7 @@
 <meta name="author" content="Tobias Gerstenberg" />
 
 
-<meta name="date" content="2024-02-28" />
+<meta name="date" content="2024-03-01" />
 
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@@ -51,6 +51,8 @@
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@@ -908,6 +910,20 @@ <h1>Page not found</h1>
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