Three-Step Estimation for Latent Class Analysis

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Documentation for package ‘tseLCA’ version 2.0.0

• DESCRIPTION file.
• User guides, package vignettes and other documentation.
• Package NEWS.

Help Pages

anova.tseLCA_covariate	Wald tests of covariate terms
as.data.frame.tseLCA_select	Class enumeration results
as_tse_lca	Use a measurement model with given parameters (Step 1)
best_model	Class enumeration results
best_model.tseLCA_select	Class enumeration results
bk2018_params	Default population parameters for the Bakk & Kuha (2018) simulation
classes	Posterior class-membership probabilities and modal class assignments
classes.tseLCA	Posterior class-membership probabilities and modal class assignments
classification	Components of a fitted tseLCA model
classification.tseLCA	Components of a fitted tseLCA model
class_sizes	Class sizes and item-response probabilities of the measurement model
class_sizes.tseLCA	Class sizes and item-response probabilities of the measurement model
coef.summary.tseLCA_structural	Summarize a fitted tseLCA model
coef.tseLCA_measurement	Coefficients of a fitted tseLCA model
coef.tseLCA_structural	Coefficients of a fitted tseLCA model
covariate	Components of a fitted tseLCA model
covariate.tseLCA	Components of a fitted tseLCA model
distal	Components of a fitted tseLCA model
distal.tseLCA	Components of a fitted tseLCA model
draw_classes	Draw latent class memberships from their marginal distribution
draw_classes_given_Zp	Draw latent classes conditional on the covariate (scenario "covariate")
draw_indicators	Draw binary indicators given true class memberships
draw_Zo	Draw a continuous distal outcome given true class memberships (scenario "distal")
draw_Zp	Draw the covariate Zp ~ Uniform{1, 2, 3, 4, 5}
fitted.tseLCA_measurement	Class membership predictions from a measurement model
fitZ_from_fit0	Estimate covariate effects with measurement parameters fixed (two-step EM)
fitZ_from_multiLCA	Estimate two-step covariate model with multilevLCA (optional reference path)
generate_all_conditions	Generate datasets for all 18 conditions in the simulation design
generate_data	Generate one dataset following the Bakk & Kuha (2018) simulation design
item_probs	Class sizes and item-response probabilities of the measurement model
item_probs.tseLCA	Class sizes and item-response probabilities of the measurement model
lca_step1	Fit the LCA measurement model (Step 1)
logLik.tseLCA	Log-likelihood, number of observations, and information criteria
make_rho	Build the item-response probability matrix for the simulation
measurement	Components of a fitted tseLCA model
measurement.tseLCA	Components of a fitted tseLCA model
mnl_probs	Compute multinomial logistic class probabilities given covariates
nobs.tseLCA	Log-likelihood, number of observations, and information criteria
omnibus_test	Omnibus Wald test of class equality for a distal outcome
omnibus_test.tseLCA_both	Omnibus Wald test of class equality for a distal outcome
omnibus_test.tseLCA_distal	Omnibus Wald test of class equality for a distal outcome
plot.tseLCA	Plot item-response probability profiles for a tseLCA model
plot.tseLCA_select	Class enumeration results
posterior	Posterior class-membership probabilities and modal class assignments
posterior.tseLCA	Posterior class-membership probabilities and modal class assignments
predict.tseLCA_covariate	Class-membership probabilities from a covariate model
predict.tseLCA_measurement	Class membership predictions from a measurement model
print.summary.tseLCA_measurement	Summarize a fitted tseLCA model
print.summary.tseLCA_structural	Summarize a fitted tseLCA model
print.tseLCA_classify	Assign observations to latent classes (Step 2)
print.tseLCA_measurement	Summarize a fitted tseLCA model
print.tseLCA_select	Class enumeration results
print.tseLCA_structural	Summarize a fitted tseLCA model
relevel.tseLCA_covariate	Change the reference class of a covariate model
summary.tseLCA_measurement	Summarize a fitted tseLCA model
summary.tseLCA_structural	Summarize a fitted tseLCA model
three_step	Three-step LCA estimation with covariates and/or distal outcomes
tseLCA	Three-step latent class analysis in one call
tse_classify	Assign observations to latent classes (Step 2)
tse_control	Estimation settings for tseLCA models
tse_covariate	Relate latent classes to covariates (Step 3)
tse_distal	Relate latent classes to a distal outcome (Step 3)
tse_lca	Fit a latent class measurement model (Step 1)
tse_twostep	Two-step estimates of covariate effects
vcov.tseLCA_measurement	Variance-covariance matrix of a fitted tseLCA model
vcov.tseLCA_structural	Variance-covariance matrix of a fitted tseLCA model
[[.tseLCA_select	Class enumeration results