Quickstart

Installation

pip install polca

Basic Usage

The simplest way to fit a latent class model is with the pypolca.fit() function and a formula string:

from pypolca import fit, load_dataset

data = load_dataset("CHEATING")
result = fit("cbind(GPA, LIEEXAM, LIEFOOL, LIEMARCH) ~ 1", data, nclass=2)

print(result)
# Model converged in 28 iterations
# log-likelihood: -435.99
# AIC: 891.98, BIC: 916.06
# Class shares: 0.77, 0.23

Formula Syntax

Outcome variables are wrapped in cbind(...). Covariates go after ~:

• cbind(Y1, Y2) ~ 1 — no covariates (intercept only)

• cbind(Y1, Y2, Y3) ~ COV1 + COV2 — two covariates

• cbind(Y1) ~ SEX + AGE — single outcome with covariates

Results Object

The pypolca.LCAResult object provides everything you need:

result.probs          # class-conditional response probabilities
result.posterior      # posterior class membership (N × nclass)
result.predclass      # modal class assignment
result.loglik         # log-likelihood
result.aic, result.bic  # information criteria
result.coeff           # covariate coefficients (beta)

Low-Level API

For advanced use, you can call the C++ functions directly:

from pypolca import Data, compute_ylik, fit_em

data = Data(y, x, num_choices)
results = fit_em(data, nclass=3, maxiter=1000, tol=1e-8)