plot.multilpa_transitions: Plot a fitted latent transition model

In latents: Multilevel Latent Profile, Class and Transition Models

Plot a fitted latent transition model

Description

The measurement model is the one multilpa() fits, so every measurement and classification view it draws is available here. what = "transitions" is the view this family adds: the estimated transition matrix, one panel per group class, with a row that has no data support labelled in parentheses because it is uniform by construction rather than estimated.

Usage

## S3 method for class 'multilpa_transitions'
plot(
  x,
  what = c("transitions", "profiles", "bars", "heatmap", "responses", "sequences",
    "sizes", "entropy", "posteriors", "avepp", "all"),
  data = NULL,
  scale = c("raw", "standardized"),
  category = "last",
  labels = TRUE,
  cell_labels = TRUE,
  main = NULL,
  subtitle = NULL,
  ...
)

Arguments

x	A fitted multilpa_transitions model.
what	The view to draw. "transitions" draws the estimated transition matrix, one panel per group class. "profiles", "bars" and "heatmap" draw the measurement model, "responses" the categorical response curves, "sequences" each group's profile at each occasion, and "sizes", "entropy", "posteriors" and "avepp" the classification diagnostics. "all" returns every view this fit has the ingredients for.
data	Optional. Accepted for consistency with plot.multilpa(); this family's views draw point estimates, and parameter_inference() reports the standard errors.
scale	"raw" keeps the indicators in their own units; "standardized" divides by each indicator's observed standard deviation.
category	For "responses", which category to draw.
labels	Whether to label series directly.
cell_labels	For "sequences", whether to print the profile number in each cell.
main, subtitle	Title and subtitle. NULL uses the view's own.
...	Nothing further is accepted; an unknown argument raises an error of class latents_bad_argument.

Value

A ggplot object; for what = "all", a latents_plots list.

See Also

get_tna() and get_group_tna() to draw the transitions as a network instead, and plot.multilpa() for the same views on a cross-sectional fit.

Examples

if (requireNamespace("ggplot2", quietly = TRUE)) {
  fit <- lta(subset(course_engagement, student <= 40),
             c("browse", "lectures", "forum_read"), "student",
             n_profiles = 2, time = "sequence", n_starts = 2)
  plot(fit, what = "transitions")
  plot(fit, what = "profiles")
}

latents documentation built on Oct. 16, 2026, 5:09 p.m.