Caterpillar plot of random effects

Displays the conditional modes ("BLUPs") of a mixed model's random effects as a sorted point-and-interval ("caterpillar") plot. It is the usual way to inspect by-group departures from the average, and to identify unusual groups.

Usage

random_effects_plot(
  x,
  conf_level = 0.95,
  sort = TRUE,
  point_colour = depictr_brand(),
  title = NULL,
  x_lab = "Random effect"
)

Arguments

x

Either a mixed model fitted with 'lme4' (merMod), or a data frame with one row per group level and columns such as level/group, estimate, and either conf.low/conf.high or std.error. An optional term column facets the plot. A model may have several grouping factors and several terms per factor, correlated or not. The forms (1 + x | g), (1 + x || g) and (1 | g) + (0 + x | g) are all drawn. The intervals are the conditional modes plus or minus the normal quantile times the conditional standard deviations that lme4::ranef(condVar = TRUE) reports. A model with one term in all is drawn in a single panel labelled "Random effect", and otherwise each term has a panel labelled "factor: term".

conf_level

Confidence level when intervals are derived from standard errors, a single number strictly between 0 and 1.

sort

Whether to order the levels by their estimate.

point_colour

Colour for the points and intervals.

title, x_lab

Plot title and value-axis label.

Value

A ggplot2::ggplot object.

Examples

# From a data frame of by-group estimates:
re <- data.frame(
  level = paste0("G", 1:10),
  estimate = sort(rnorm(10)),
  std.error = runif(10, 0.2, 0.5)
)
random_effects_plot(re)


# \donttest{
if (requireNamespace("lme4", quietly = TRUE)) {
  m <- lme4::lmer(RT ~ condition + (1 | participant), data = lexical_decision)
  random_effects_plot(m)
}

# }