| get_results.multilpa_cross_level | R Documentation |
Tidy tables of a fit from multilpa(family = "restricted_cross_level") or
multilpa(family = "full_cross_level").
## S3 method for class 'multilpa_cross_level'
get_results(x, what = "profiles", ...)
## S3 method for class 'multilpa_cross_level'
as.data.frame(x, row.names = NULL, optional = FALSE, what = "profiles", ...)
## S3 method for class 'multilpa_cross_level'
print(x, ...)
## S3 method for class 'multilpa_cross_level'
summary(object, ...)
## S3 method for class 'multilpa_cross_level'
logLik(object, ...)
## S3 method for class 'multilpa_cross_level'
nobs(object, ...)
x |
A |
what |
Which table; |
... |
Unused. |
row.names, optional |
Unused; part of the generic. |
object |
A |
A base data.frame, or a named list of them for what = "all".
profilesOne row per individual profile and indicator: level = "individual", profile, indicator, mean, variance; then one row
per group class and indicator for the group means: level = "group",
the class in profile, and the class's mean and variance of the
group means.
group_classesOne row per group class: weight, count
(summed posterior) and n_assigned.
compositionOne row per group class and profile: the share of the class's members in each profile. Model-implied for the full family; for the restricted family a posterior-weighted description, since its prevalences do not depend on the class.
groupsOne row per group: size, modal group class, its posterior and every class probability.
assignmentsOne row per data row: its modal profile and
posterior, and its group's modal group_class (inherited).
fitOne row: family, log likelihood, n_parameters, aic,
bic (by groups), bic_individual (by rows), sizes, convergence and
boundary. The likelihood is the working likelihood of the manifest
specification (the group means are computed from the same ratings), so
it is comparable only between cross-level fits of the same data.
startsThe start records (full family) or the two parts' likelihoods (restricted family).
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, sample(c(-1, 1), 240, replace = TRUE), 0.6))
fit <- multilpa(ratings, "climate", "team", n_profiles = 2,
n_group_classes = 2, family = "full_cross_level",
n_starts = 3, seed = 1)
get_results(fit)
get_results(fit, "composition")
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