glmer_effects.R

# Helper function 
# gets a fitted glmer model 
# Example:
# m0 <- glmer(acc ~ cond + (0 + rt | subject_nr) + (1 | item) + (1 | subject_nr), family=binomial(link=logit), data = datos.x.rt)
# Wald is fast and innacurate, better try the longer "profile"


plot.glmer <- function(m0, my.method = "Wald") {
  # that could take a while 
  confint_m0 <- confint(m0, level = 0.95, method=my.method)
  # as family is binomial(link=logit), you must convert with plogis() to a human 
  # readable form
  confint_m0 <- as.data.frame(plogis(confint_m0))
  # changes the colnames form the 2.5% and 97.5% to lower and upper
  colnames(confint_m0) <- c("lower", "upper")
  # get the effect
  confint_m0$fixef <- plogis(fixef(m0))
  # names the vars
  confint_m0$vars <- levels(m0@frame$cond)
  confint_m0
  
}
confint_m0 <- plot.glmer(m0)
# Makes the plot 
(plotm0 <- ggplot(confint_m0, aes( x= vars , y = fixef)) + geom_errorbar(aes(ymin=lower, ymax=upper)) + 
  ggtitle(paste(attr(m0@frame, "formula")[3])) +
  ylim(0,1) + theme_bw())