Plot a covariate model

Draws the parts of a covariate fit that are still fixed quantities: the measurement model, the assignments and the classification diagnostics, as plot.multilpa() draws them. Profile prevalence cannot be drawn: a covariate model has no single prevalence vector, because prevalence varies with each unit's covariates, so asking for it is refused rather than answered with an average that no unit has.

Usage

# S3 method for class 'multilpa_covariates'
plot(
  x,
  what = c("profiles", "bars", "heatmap", "raincloud", "parallel", "pairs", "responses",
    "sequences", "sizes", "entropy", "posteriors", "avepp", "all"),
  data = NULL,
  scale = c("raw", "standardized"),
  category = "last",
  labels = TRUE,
  intervals = TRUE,
  cell_labels = TRUE,
  main = NULL,
  subtitle = NULL,
  statistic = c("mean", "median", "mode"),
  ...
)

Arguments

x

A fitted multilpa_covariates model.

what

The view; every view of plot.multilpa() except "probabilities".

data, scale, category, labels, intervals, cell_labels, main, subtitle, statistic, ...

As in plot.multilpa().

Value

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

Examples

if (requireNamespace("ggplot2", quietly = TRUE)) {
  set.seed(5)
  school <- rep(seq_len(16), each = 8)
  high_class <- rep(rep(c(FALSE, TRUE), length.out = 16), each = 8)
  x <- rnorm(128)
  profile <- ifelse(
    runif(128) < plogis(-1 + 2 * high_class + 0.8 * x), 2L, 1L
  )
  example_data <- data.frame(
    school = school, x = x,
    y1 = rnorm(128, ifelse(profile == 2L, 2, -2), 0.7),
    y2 = rnorm(128, ifelse(profile == 2L, 1.5, -1.5), 0.7)
  )
  fit <- multilpa(example_data, c("y1", "y2"), "school", n_profiles = 2,
                  n_group_classes = 2, profile_covariates = "x",
                  n_starts = 2)
  plot(fit)
}