candidate_fit: Take one fitted model out of an enumeration grid

In latents: Multilevel Latent Profile, Class and Transition Models

Take one fitted model out of an enumeration grid

Description

An enumeration keeps every candidate it fitted. This returns one of them, so that a caller who wants to plot, summarise or test a particular candidate names it by its class counts instead of indexing the grid by position.

Usage

candidate_fit(x, n_profiles, n_group_classes = 1L, model = NULL)

Arguments

x	An multilpa_enumeration result from enumerate_classes().
n_profiles	Number of individual profiles identifying the candidate.
n_group_classes	Number of group classes identifying the candidate.
model	The covariance model identifying the candidate, needed when enumerate_classes() was given several models and the class counts alone name several candidates. Naming counts that match more than one candidate raises latents_unknown_candidate listing the models it could have meant.

Value

The fitted model for that cell of the grid: an object of class multilpa, exactly as multilpa() returned it, with every verb of this package available on it.

Conditions

latents_unknown_candidate when the requested class counts are not in the grid, and latents_failed_candidate when they are in the grid but that fit raised an error, so no model exists to return. Neither situation returns NULL, because a NULL would flow silently into whatever the caller did next.

Examples

set.seed(1)
d <- data.frame(g = rep(1:10, each = 10), y = rnorm(100))
candidates <- enumerate_classes(d, "y", "g", n_profiles = 1:2,
                              n_group_classes = 1, n_starts = 2, seed = 1)
candidate_fit(candidates, n_profiles = 2, n_group_classes = 1, model = "VVI")

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