Model types

Latent class models

Latent profile models

Finite mixture models

Path models with categorical latent variables

Multiple-group models with known groups

 

Categorical latent variables measured by

Binary items

Ordinal items

Continuous items

Count items

Categorical items

Fractional items

Survival times

 

Model class membership

Predictors of class membership

Multinomial logistic model

 

Starting values

EM algorithm

Fixed or random starting values

Select number of random draws

 

Constraints

Easily specify equality constraints across classes

Constrain one parameter

Cross-class equality constraints—just type lcinvariant(cons) to constrain intercepts

 

Multiple-group models

Allow for differences in LCA across known groups

Group estimation is as easy as group(agegroup)

Some parameters constrained and others estimated freely across groups

 

Goodness of fit

Likelihood-ratio test vs saturated model (G2 statistic)

AIC

BIC

Inferences

Expected means, probabilities, or counts in each class

Expected proportion of population in each class

AIC and BIC information criteria

Wald tests of linear and nonlinear constraints

Likelihood-ratio tests

Contrasts

Pairwise comparisons

Linear and nonlinear combinations of coefficients with SEs and CIs

 

Predictions

Class membership

Posterior class membership

Predicted means, probabilities, counts

For each latent class

Marginal with respect to latent classes

Marginal with respect to posterior latent classes

Survivor function

Density function

Distribution function

 

Postestimation Selector

View and run all postestimation features for your command

Automatically updated as estimation commands are run

 

Factor variables

Automatically create indicators based on categorical variables

Form interactions among discrete and continuous variables

Include polynomial terms

Perform contrasts of categories/levels

 

Marginal analysis

Estimated marginal means

Marginal and partial effects

Average marginal and partial effects

Adjusted predictions, means, and effects

Works with multiple outcomes simultaneously

Contrasts of margins

Pairwise comparisons of margins

Profile plots

Graphs of margins and marginal effects