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Compare latent class models

Source: R/lpa.R
enumerate_lca.Rd

Fits single-level latent class models with each number of classes in n_classes and returns them in one table for comparison by information criteria, entropy and the diagnostics of each fit. Every indicator is categorical unless named in ordinal or count, as in lca(). It is enumerate_classes() with id = NULL, and it returns the same object; the number of classes is its n_profiles column.

Usage

enumerate_lca(data, vars, n_classes = 1:4, ...)

Arguments

data

A data frame with one row per observation.

vars

Names of the categorical indicator columns.

n_classes

Positive whole numbers of classes to compare.

...

Further arguments for multilca(), such as missing, n_starts, seed and min_probability, except id, n_group_classes and categorical.

Value

An object of class multilpa_enumeration, as enumerate_classes() returns: as.data.frame() gives one row per candidate, summary() the candidate each criterion prefers, plot() the criteria against the number of classes, and candidate_fit() one fitted candidate, named by its n_profiles.

Details

For observations nested in groups, use enumerate_classes() with the grouping column as id and the indicators as categorical.

Conditions

latents_bad_argument when id, n_group_classes, categorical or model is passed.

See also

lca() to fit one model; enumerate_lpa() for continuous indicators.

Examples

models <- enumerate_lca(student_esm, c("time_with_friends",
                                       "on_social_media", "sports"),
                        n_classes = 1:3, n_starts = 2, seed = 1)
summary(models)
#> Class enumeration: 3 candidates, 3 converged, 0 failed to fit
#> 3 distinct candidate(s) are minimal under some criterion.
#> No candidate is selected automatically. Choose one convention and keep it.
#> The candidates table shows 9 of its 26 columns; get_results(x, what = "candidates") returns all of them.
#> 
#> -- candidates ------------------------------------------------------
#>  n_profiles log_likelihood n_parameters  aic  bic icl_individual
#>           1          -3280            3 6566 6584           6584
#>           2          -3269            7 6552 6593           7454
#>           3          -3269           11 6560 6624           6651
#>  profile_entropy converged boundary
#>               NA      TRUE    FALSE
#>           0.7594      TRUE    FALSE
#>           0.9953      TRUE    FALSE
#> 
#> -- criteria --------------------------------------------------------
#>  criterion  convention n_profiles n_group_classes model value
#>        aic        <NA>          2               1  <NA>  6552
#>        kic        <NA>          2               1  <NA>  6562
#>        bic      groups          1               1  <NA>  6584
#>        bic individuals          1               1  <NA>  6584
#>      sabic      groups          2               1  <NA>  6571
#>      sabic individuals          2               1  <NA>  6571
#>       caic      groups          1               1  <NA>  6587
#>       caic individuals          1               1  <NA>  6587
#>        awe      groups          1               1  <NA>  6616
#>        awe individuals          1               1  <NA>  6616
#>    ... 4 more rows.  get_results(x, what = "criteria")
#> 
#> 2 tables above, truncated to fit. get_results(x, what = ) returns any
#> of them whole, and get_results(x, what = "all") returns every one.