Create density plots for mixture models

Creates a density plot for a single object of class 'mplus.model', or a faceted plot of density plots for an object of class 'mplus.model.list'. For each variable, a Total density plot will be shown, along with separate density plots for each latent class, where cases are weighted by the posterior probability of being assigned to that class.

plotMixtureDensities(
  modelList,
  variables = NULL,
  bw = FALSE,
  conditional = FALSE,
  alpha = 0.2,
  facet_labels = NULL
)

Arguments

modelList	A list object of Mplus models, or a single Mplus model
variables	Which variables to plot. If NULL, plots all variables that are present in all Mplus models.
bw	Logical. Whether to make a black and white plot (for print) or a color plot. Defaults to FALSE, because these density plots are hard to read in black and white.
conditional	Logical. Whether to show a conditional density plot (surface area is divided amongst the latent classes), or a classic density plot (surface area of the total density plot is equal to one, and is subdivided amongst the classes).
alpha	Numeric (0-1). Only used when bw and conditional are FALSE. Sets the transparency of geom_density, so that classes with a small number of cases remain visible.
facet_labels	Named character vector, the names of which should correspond to the facet labels one wishes to rename, and the values of which provide new names for these facets. For example, to rename variables, in the example with the 'iris' data below, one could specify: facet_labels = c("Pet_leng" = "Petal length").

Value

An object of class 'ggplot'.

Note

This function returns warnings, indicating that sum(weights) != 1. These can be ignored. The sum of the "Total" density per variable per model is equal to 1, and the sum of all of the posterior probabilities is equal to 1. This results in a normal density plot for the "Total", which is subdivided by the latent classes, in proportion to the posterior probabilities of participants being assigned to those clases.

Author

Caspar J. van Lissa

Examples

if (FALSE) {
results <- createMixtures(classes = 1:3, filename_stem = "iris",
                          rdata = iris, run = 1L)
plotMixtureDensities(results)
}
if (FALSE) {
plotMixtureDensities(results, variables = "PETAL_LE")
}
if (FALSE) {
plotMixtureDensities(results, bw = TRUE)
}
if (FALSE) {
plotMixtureDensities(results, bw = FALSE, conditional = TRUE)
}
if (FALSE) {
plotMixtureDensities(results[[2]], variables = "PETAL_LE")
}