lca_step1: Fit the LCA measurement model (Step 1)

In tseLCA: Three-Step Estimation for Latent Class Analysis

Fit the LCA measurement model (Step 1)

Description

Estimates the latent class measurement model with multilevLCA and optionally, fixes mPhi and estimates covariate effects (two-step initialization) with fitZ_from_fit0().

Usage

lca_step1(
  data,
  Y.names,
  n_classes,
  Zp.names = NULL,
  maxIter.measurement = 5000L,
  measurement.tol = 1e-08,
  covariate.tol = 1e-06,
  iter.measurement = 10L,
  R2.threshold = 0.7,
  use.two.step = TRUE,
  estimate.one.step = TRUE,
  incomplete = FALSE,
  maxIter.fitZ = 200L,
  include.intercept = TRUE,
  rebase = "C1",
  startval = NULL,
  n_init = NULL,
  verbose = FALSE
)

Arguments

data	A data.frame containing at minimum the indicator columns.
Y.names	Character vector of item column names.
n_classes	Integer. Number of latent classes.
Zp.names	Character vector of covariate column names, or NULL.
maxIter.measurement	Maximum EM iterations before giving up on convergence. Default 5000L.
measurement.tol	Convergence tolerance. Default 1e-8.
covariate.tol	Convergence tolerance for the fitZ M-step. Default 1e-6.
iter.measurement	Number of random restarts when entropy R^2 is low. Default 10.
R2.threshold	Entropy R^2 below which restarts are triggered. Default 0.7.
use.two.step	Logical. If TRUE, also estimate fitZ with fitZ_from_fit0() if Zp.names is applied. Default TRUE.
estimate.one.step	Logical. If FALSE, skip the unconditional EM and only compute fitZ. Default TRUE.
incomplete	Logical. FIML for partially missing indicators. See the Missing Data section of vignette("tseLCA", package = "tseLCA"). Default FALSE.
maxIter.fitZ	Maximum EM iterations for fitZ_from_fit0(). Default 200.
include.intercept	Logical. Prepend intercept to covariate design matrix. Default TRUE.
rebase	Character or integer specifying the reference latent class. Use "C1", "C2", etc. or an integer index. Default "C1". The measurement model is permuted so this class becomes column 1, making it the reference for all downstream multinomial logit parameterizations.
startval	Optional starting classification for the Step-1 measurement model: either an integer vector of length nrow(data) (⁠1..n_classes⁠ per row) or a numeric matrix of conditional item-response probabilities from which a classification is derived. See lca_step1_startval() for the full description of both forms. When supplied, lca_step1() fits the measurement model with lca_step1_startval(), not multilevLCA's default k-means-on-principal-components initialization, and estimate.one.step, iter.measurement, and R2.threshold (which govern the default restart-on-low-entropy behavior) are ignored. Mutually exclusive with n_init. Default NULL.
n_init	Optional positive integer. If supplied, fits the measurement model n_init times from independent uniform-random classifications (each through startval-style injection with kmea = FALSE, not multilevLCA's k-means-on-PCA path) and keeps the fit with the highest log-likelihood – the unconditional multi-start analog of n_init in StepMix or nrep in poLCA. Unlike iter.measurement (which reruns multilevLCA's own k-means initialization, and only when entropy R^2 is low), all n_init fits are always run. estimate.one.step, iter.measurement, and R2.threshold are ignored when n_init is supplied. Mutually exclusive with startval. Default NULL.
verbose	Logical. Print progress messages. Default FALSE.

Value

A list with ⁠$fit0⁠ (multilevLCA::multiLCA() measurement model) and ⁠$fitZ⁠ (two-step covariate model from fitZ_from_fit0(), or NULL).

Deprecated

Deprecated as of tseLCA 2.0.0: use tse_lca(). It keeps working and warns once per session when called directly.

Examples

d <- generate_data(200, "high", "covariate", seed = 1)

# Measurement model only
s1 <- lca_step1(d, Y.names = paste0("Y", 1:6), n_classes = 3)
s1$fit0$vPi    # estimated class prevalences
s1$fit0$mPhi   # item-response probabilities

# With two-step covariate initialization
s1z <- lca_step1(d, Y.names = paste0("Y", 1:6), n_classes = 3,
                 Zp.names = "Zp", use.two.step = TRUE, verbose = TRUE)
s1z$fitZ$mGamma   # two-step gamma estimates

# Many random-classification restarts, keeping the best (analogous to
# n_init in StepMix or nrep in poLCA)
s1r <- lca_step1(d, Y.names = paste0("Y", 1:6), n_classes = 3,
                 n_init = 20L, verbose = TRUE)

tseLCA documentation built on Nov. 11, 2026, 9:07 a.m.