Plot a growth mixture model

Usage

# S3 method for class 'latents_growth_mixture'
plot(
  x,
  what = c("trajectories", "individuals", "coefficients", "random", "classification",
    "posteriors"),
  time = NULL,
  facet = FALSE,
  persons = 12L,
  max_persons = 120L,
  spread = 0.8,
  level = 0.95,
  main = NULL,
  subtitle = NULL,
  ...
)

Arguments

x: A latents_growth_mixture fit.
what: "trajectories" (each class's mean trajectory with its confidence band, the spread of its persons' own trajectories, and the observed class means at each time), "individuals" (a sample of persons across each class's range of classification certainty: observations, their class trajectory dashed, their own predicted curve solid), "coefficients" (estimates with confidence intervals and p-values, by term), "random" (predicted random effects with the class's model-implied 95% ellipse; degenerate covariances are flagged), or "classification" (one bar per person, split by their posterior class probabilities). "posteriors" is a synonym of "classification".
time: The numeric variable to draw along; by default the first numeric variable of random.
facet: For "trajectories", TRUE draws one panel per class.
persons: For "individuals", how many persons to show, or a vector of their identifiers.
max_persons: For "trajectories", the most persons drawn behind the class trajectories (an even sample by class); 0 draws none.
spread: For "trajectories", the share of persons' class-implied trajectories the light band holds (from the random effects); 0 hides it.
level: Confidence level of the bands and intervals.
main, subtitle: Title and subtitle; defaults describe the view.
...: Unused.

Value

A ggplot object. Raises latents_missing_package without ggplot2.

Examples

few_students <- subset(growth_scores, student <= 60)
fit <- mixture_regression(score ~ wave, few_students, n_classes = 2,
                          id = "student", class_level = "group",
                          random = "intercept", seed = 1)
if (requireNamespace("ggplot2", quietly = TRUE)) plot(fit)