diagnostics: Every classification diagnostic, in one call

In latents: Multilevel Latent Profile, Class and Transition Models

Every classification diagnostic, in one call

Description

summary() reports what a model estimated. This reports whether to believe it: how sharply the posterior separates the classes, how big each class actually is, how confidently each unit was assigned, and whether the within-profile independence the model assumes survives contact with the data.

Usage

diagnostics(x, data = NULL, plots = TRUE, by = c("profile", "overall"), ...)

## S3 method for class 'multilpa_diagnostics'
as.data.frame(x, row.names = NULL, optional = FALSE, ...)

## S3 method for class 'multilpa_diagnostics'
print(x, ...)

## S3 method for class 'multilpa_diagnostics'
plot(x, ...)

Arguments

x	A fitted model of this package.
data	Optional. The data the model was fitted to. A fit carries the columns it was built from, so this is only needed to override them.
plots	TRUE, the default, also draws the four classification plots (effective profile sizes, posterior probabilities of the assigned profiles, case entropy, and the average posterior probability matrix), as printing plot() on the result does. Without the ggplot2 package a message says so and the tables are still returned. FALSE returns the tables only.
by	"profile", the default, assesses each profile's bivariate residuals separately; "overall" pools them. It is the one argument of a gathered table this function forwards, because it is the one that changes what a gathered table means rather than which fit it is taken from. The level the classification tables use is not an argument here: it follows from whether the fit has discrete group classes.
...	For diagnostics(), nothing further is accepted. An argument this function cannot forward raises an error of class latents_bad_argument naming it, rather than being dropped on the way to a table that then means something other than what was asked for. For plot(), nothing further is accepted either; style the returned plots with ggplot2.
row.names, optional	Passed to the base generic; row.names is applied to the returned table.

Details

It gathers the four classification tables of get_results() — "entropy", "classification", "average_posteriors" and "residuals" — and prints one line of reading per diagnostic rather than four differently shaped tables. The tables themselves come back from get_results() on the result, or on the fit.

Value

An object of class multilpa_diagnostics. Read its tables with get_results(result, what = ), which offers "entropy", "classification", "average_posteriors", "residuals" and "all", and never with $. A model family that has no bivariate residuals leaves that table out of "all", and asking for it by name raises latents_no_group_classes.

print() returns the object invisibly, having printed one line per diagnostic: relative entropy, smallest class, lowest average posterior and largest residual, at each level the fit has. plot() returns every classification plot as a latents_plots list of ggplot objects – the effective profile sizes, the posterior probability of each case's assigned profile, each case's relative entropy, and the average posterior probability matrix – that draws all four when printed. as.data.frame() returns the entropy table, the primary one.

See Also

get_results() for these tables and every other one, descriptives() for the before-the-fit counterpart, and summary() for what the model estimated rather than whether to trust it.

Examples

fit <- multilpa(
  course_engagement,
  vars = c("browse", "lectures", "forum_read", "forum_post", "attendance"),
  id = "student", n_profiles = 2, n_group_classes = 2, n_starts = 4,
  seed = 1
)
quality <- diagnostics(fit)
quality
get_results(quality, what = "classification")
get_results(diagnostics(fit, by = "overall"), what = "residuals")

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