get_results.multilpa_additive: Tables of an additive group-class fit

In latents: Multilevel Latent Profile, Class and Transition Models

Tables of an additive group-class fit

Description

Tidy tables of a fit from multilpa(family = "additive"). Every table is a base data.frame; the fit itself never needs to be reached into.

Usage

## S3 method for class 'multilpa_additive'
get_results(
  x,
  what = "parameters",
  level = 0.95,
  vcov_type = c("observed", "robust", "opg"),
  adjust = .multilpa_p_adjust_methods,
  data = NULL,
  truth = NULL,
  ...
)

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

## S3 method for class 'multilpa_additive'
coef(object, scale = c("natural", "unconstrained"), ...)

## S3 method for class 'multilpa_additive'
vcov(
  object,
  type = c("observed", "robust", "opg"),
  scale = c("natural", "unconstrained"),
  ...
)

## S3 method for class 'multilpa_additive'
confint(object, parm, level = 0.95, type = c("observed", "robust", "opg"), ...)

## S3 method for class 'multilpa_additive'
logLik(object, ...)

## S3 method for class 'multilpa_additive'
nobs(object, ...)

Arguments

x	A multilpa_additive fit.
what	Which table; "all" returns a named list of every table.
level	Confidence level of the intervals in "parameters".
vcov_type	"observed" (inverse observed information), "robust" (CR0 sandwich over groups) or "opg" (inverse outer product of the group scores).
adjust	p-value adjustment across the class means, one of stats::p.adjust.methods; "none" by default, as elsewhere in latents.
data, truth	For what = "recovery" only: the data the fit was made from and the name of a column, constant within groups, holding a known group classification.
...	Unused.
row.names, optional	Unused; part of the generic.
object	A multilpa_additive fit.
scale	"natural" (means, variances, class weights) or "unconstrained" (means, log variances, class-1-baseline logits). Names of coef() and the rows of vcov() agree on either scale.
type	Covariance type, as vcov_type.
parm	Natural parameter names or positions; all by default.

Value

A base data.frame, or a named list of them for what = "all".

Tables

parameters

One row per natural parameter: level ("between" for class means and between-group variances, "within" for the shared within-group variances, "group_class" for class weights), group_class ("shared" for a parameter common to all classes), indicator (NA for weights), parameter ("mean", "variance", "weight"), estimate, standard_error, statistic, p_value, p_adjusted, conf_low, conf_high. Only means carry a Wald test. Intervals for variances are formed on the log scale and for weights on the logit scale. When inference is refused (boundary, unconverged, singular information) the standard-error columns are NA and a latents_no_standard_errors message says why.

group_classes

One row per group class: weight, count (summed posterior), n_assigned (modal), mean_posterior among the groups assigned to it, and effective_groups: the number of perfectly classified groups that would estimate the class weight as precisely as this fit does, w^2 (1 - w) / Var(w). It equals the class's expected group count when classes are perfectly separated and falls when a class is small or poorly separated. Below 50, intervals for class parameters are flagged with a latents_weak_class warning (threshold and its simulation evaluation: validation/ADDITIVE_SIMULATION.md in the source repository). NA for one class or when inference is refused.

groups

One row per group (named by the id column): size n, modal group_class, its posterior, and probability_group_class_h.

intercepts

One row per group and indicator: n, the observed group average, and the posterior mean and variance of the group's intercept, mixed over its class posterior (so the variance includes uncertainty about class membership). Conditional on the estimated parameters; not a statement about their sampling uncertainty.

conditional_intercepts

As intercepts, one row per group, indicator and group class, conditional on that class, with the class posterior.

assignments

One row per data row: row, the id column, the group's modal group_class and posterior, and assignment_level = "group": every row inherits its group's class.

classification

One row per assigned and group class: the mean_posterior of group_class among groups assigned to assigned.

fit

One row: log likelihood, n_parameters, aic, bic (penalized by the number of groups), bic_individual (by rows), n_groups, n_obs, relative entropy over groups, the smallest class weight, min_effective_groups, converged, boundary, kkt and start replication.

recovery

Needs data and truth: one row per modal group class and known value, with the number of groups n and their share of the assigned class. Not part of what = "all".

starts

One row per start: likelihood, convergence, iterations, number of between variances held at zero, error message of a failed start, and which start was selected.

Examples

set.seed(1)
ratings <- data.frame(
  team = rep(seq_len(40), each = 6),
  climate = rep(rnorm(40, rep(c(-1, 1), each = 20), 0.5), each = 6) +
    rnorm(240),
  support = rep(rnorm(40, rep(c(-1, 1), each = 20), 0.5), each = 6) +
    rnorm(240))
fit <- multilpa(ratings, c("climate", "support"), "team",
                n_group_classes = 2, family = "additive", n_starts = 3,
                seed = 1)
get_results(fit)
get_results(fit, "group_classes")
get_results(fit, "parameters", vcov_type = "robust")

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