Summarise latents results
Source:R/additive-methods.R, R/covariates.R, R/enumeration.R, and 6 more
latents-summary.RdSummary methods for fitted models, enumerations, bootstrap tests, covariate and transition models, mixture regressions and pooled imputations. A summary collects the model's main tables and diagnostics; printing it shows each table to a set depth.
Usage
# S3 method for class 'multilpa_additive'
summary(object, level = 0.95, vcov_type = c("observed", "robust", "opg"), ...)
# S3 method for class 'multilpa_covariates'
summary(object, ...)
# S3 method for class 'multilpa_enumeration'
summary(object, ...)
# S3 method for class 'multilpa_bootstrap_lrt'
summary(object, ...)
# S3 method for class 'latents_growth_mixture'
summary(object, level = 0.95, vcov_type = NULL, ...)
# S3 method for class 'multilpa'
summary(object, ...)
# S3 method for class 'latents_mixture_regression'
summary(object, level = 0.95, vcov_type = NULL, ...)
# S3 method for class 'latents_pooled'
summary(object, ...)
# S3 method for class 'multilpa_transitions'
summary(object, ...)Arguments
- object
An object returned by a latents function.
- level
Confidence level for the reported intervals, strictly between zero and one.
- vcov_type
"observed","robust"or"opg", as forparameter_inference(). For mixture regressions and growth mixtures,NULLreuses the inference stored with the fit, or, when there is none, uses robust errors for a weighted fit and observed otherwise.- ...
Ignored; present for compatibility with
summary().
Value
An object of class summary_<class> holding the summarised tables.
Print it, or read any table with get_results() or as.data.frame().
Examples
fit <- multilpa(course_engagement, c("browse", "lectures", "forum_read"),
"student", n_profiles = 2, n_group_classes = 2,
n_starts = 2, seed = 1)
print(summary(fit), rows = 3)
#> Multilevel LPA: 2 profiles and 2 group classes
#> Individuals: 1422; groups: 106; parameters: 15; converged: TRUE
#> Log likelihood: -5390.952704; AIC: 10811.905
#> BIC (groups): 10851.857; BIC (individuals): 10890.803
#> Best likelihood replicated in 2/2 starts (absolute tolerance 0.00539).
#>
#> -- profiles --------------------------------------------------------
#> profile indicator mean variance standard_deviation mean_standard_error
#> 1 browse 0.5389 0.5896 0.7679 0.02838
#> 1 lectures 0.4195 0.8398 0.9164 0.03346
#> 1 forum_read 0.6130 0.4871 0.6979 0.02750
#> variance_standard_error
#> 0.03075
#> 0.04258
#> 0.02753
#> ... 3 more rows. get_results(x, what = "profiles")
#>
#> -- responses -------------------------------------------------------
#> (no rows)
#>
#> -- covariances -----------------------------------------------------
#> profile indicator indicator_2 covariance
#> 1 browse browse 0.5896
#> 1 lectures browse 0.0000
#> 1 forum_read browse 0.0000
#> ... 15 more rows. get_results(x, what = "covariances")
#>
#> -- profile_probabilities -------------------------------------------
#> group_class profile probability group_class_probability
#> 1 1 0.7890 0.6853
#> 1 2 0.2110 0.6853
#> 2 1 0.1641 0.3147
#> ... 1 more rows. get_results(x, what = "profile_probabilities")
#>
#> -- counts ----------------------------------------------------------
#> level class effective_count effective_proportion
#> individuals 1 842.63 0.5926
#> individuals 2 579.37 0.4074
#> groups 1 72.64 0.6853
#> ... 1 more rows. get_results(x, what = "counts")
#>
#> -- posteriors ------------------------------------------------------
#> row group profile posterior modal
#> 1 1 1 0.8993756 TRUE
#> 2 1 1 0.8666222 TRUE
#> 3 1 1 0.0003331 FALSE
#> ... 2841 more rows. get_results(x, what = "posteriors")
#>
#> -- group_posteriors ------------------------------------------------
#> group group_size log_likelihood group_class posterior modal
#> 1 13 -41.08 1 4.167e-05 FALSE
#> 2 13 -44.87 1 2.542e-07 FALSE
#> 3 14 -47.99 1 1.000e+00 TRUE
#> ... 209 more rows. get_results(x, what = "group_posteriors")
#>
#> -- assignments -----------------------------------------------------
#> student browse lectures forum_read profile group_class uncertainty
#> 1 0.73 -0.08 0.30 1 2 0.1006244
#> 1 0.68 0.53 -0.02 1 2 0.1333778
#> 1 -1.51 -0.51 -0.91 2 2 0.0003331
#> posterior_profile_1 posterior_profile_2
#> 0.8993756 0.1006
#> 0.8666222 0.1334
#> 0.0003331 0.9997
#> ... 1419 more rows. get_results(x, what = "assignments")
#>
#> -- classification --------------------------------------------------
#> level class n_modal proportion_modal estimated_n estimated_proportion
#> individuals 1 837 0.5886 842.63 0.5926
#> individuals 2 585 0.4114 579.37 0.4074
#> groups 1 73 0.6887 72.64 0.6853
#> average_posterior odds_correct_classification
#> 0.9681 20.86
#> 0.9447 24.85
#> 0.9853 30.70
#> ... 1 more rows. get_results(x, what = "classification")
#>
#> -- average_posteriors ----------------------------------------------
#> level assigned_class class n_assigned average_posterior
#> individuals 1 1 837 0.96809
#> individuals 1 2 837 0.03191
#> individuals 2 1 585 0.05528
#> ... 5 more rows. get_results(x, what = "average_posteriors")
#>
#> -- classification_errors -------------------------------------------
#> level true_class assigned_class probability
#> individuals 1 1 0.96162
#> individuals 1 2 0.03838
#> individuals 2 1 0.04610
#> ... 5 more rows. get_results(x, what = "classification_errors")
#>
#> -- bch_weights -----------------------------------------------------
#> level unit assigned_class class weight
#> individuals 1 1 1 1.04192
#> individuals 1 1 2 -0.04192
#> individuals 2 1 1 1.04192
#> ... 3053 more rows. get_results(x, what = "bch_weights")
#>
#> -- entropy ---------------------------------------------------------
#> level n_classes n_units entropy_sum relative_entropy
#> individuals 2 1422 152.457 0.8453
#> groups 2 106 4.959 0.9325
#>
#> -- residuals -------------------------------------------------------
#> profile indicator_1 indicator_2 kind observed expected residual
#> profile_2 browse forum_read gaussian 0.09022 0 0.09022
#> profile_2 browse lectures gaussian -0.05080 0 -0.05080
#> profile_1 browse forum_read gaussian 0.03014 0 0.03014
#> effective_n statistic df p_value p_adjusted
#> 579.4 2.1718 NA 0.02987 0.02987
#> 579.4 -1.2206 NA 0.22224 0.22224
#> 842.6 0.8737 NA 0.38226 0.38226
#> ... 3 more rows. get_results(x, what = "residuals")
#>
#> -- information_criteria --------------------------------------------
#> log_likelihood n_parameters aic kic bic_groups bic_individual sabic_groups
#> -5391 15 10812 10830 10852 10891 10804
#> sabic_individual caic_groups caic_individual awe_groups awe_individual
#> 10843 10867 10906 10977 11350
#> icl_groups icl_individual clc_groups clc_individual
#> 10862 11196 10792 11087
#>
#> -- model -----------------------------------------------------------
#> n_observations n_informative n_groups n_profiles n_group_classes centering
#> 1422 1422 106 2 2 none
#> covariance_structure n_parameters n_parameters_with_measurement log_likelihood
#> VVI 15 15 -5391
#> aic bic_groups bic_individual converged iterations boundary small_classes
#> 10812 10852 10891 TRUE 12 FALSE FALSE
#> best_start n_best_replicated weights
#> 1 2 <NA>
#>
#> -- stages ----------------------------------------------------------
#> stage group_classes fixed log_likelihood parameters
#> joint 2 <NA> -5391 15
#> parameters_with_measurement converged
#> 15 TRUE
#>
#> -- starts ----------------------------------------------------------
#> start log_likelihood converged iterations error boundary
#> 1 -5391 TRUE 12 <NA> FALSE
#> 2 -5391 TRUE 15 <NA> FALSE
#>
#> -- data ------------------------------------------------------------
#> student browse lectures forum_read
#> 1 0.73 -0.08 0.30
#> 1 0.68 0.53 -0.02
#> 1 -1.51 -0.51 -0.91
#> ... 1419 more rows. get_results(x, what = "data")
#>
#> 19 tables above, truncated to fit. get_results(x, what = ) returns any
#> of them whole, and get_results(x, what = "all") returns every one.