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Estimate covariate effects with measurement parameters fixed (two-step EM)

Source: R/lca_measurement.R
fitZ_from_fit0.Rd

Fixes mPhi at fit0$mPhi and estimates multinomial logit coefficients mGamma (Q x (T-1)) via an EM algorithm with a BFGS M-step.

Usage

fitZ_from_fit0(
  fit0,
  data,
  Y.names,
  Zp.names,
  tol = 1e-06,
  maxIter = 200L,
  incomplete = FALSE,
  include.intercept = TRUE,
  rebase = "C1",
  starting_val = NULL,
  verbose = FALSE
)

Arguments

fit0

Output of lca_step1()$fit0.

data

A data.frame.

Y.names

Character vector of item column names.

Zp.names

Character vector of covariate column names.

tol

Convergence tolerance. Default 1e-6.

maxIter

Maximum EM iterations. Default 200.

incomplete

Logical. FIML for partially missing indicators. See the Missing Data section of vignette("tseLCA", package = "tseLCA"). Default FALSE.

include.intercept

Logical. Prepend intercept to covariate design matrix. Default TRUE.

rebase

Character or integer. Reference class for the multinomial logit parameterization (e.g. "C1", "C2", or an integer). Default "C1". Must match the rebase used in lca_step1() so class column ordering is consistent.

starting_val

Optional Q x (T-1) starting value matrix for mGamma.

verbose

Logical. Print convergence messages. Default FALSE.

Value

A list with the following elements:

mGamma

Q x (T-1) numeric matrix of multinomial logit coefficients, where Q is the number of columns in the covariate design matrix (including intercept if include.intercept = TRUE). Rows are named by covariate, columns by non-reference class (e.g. "C2", "C3").

mPhi

Expanded item parameter matrix (items x classes), fixed at fit0$mPhi throughout estimation.

vOmega

Length-T vector of marginal class proportions implied by the final mGamma, computed as column means of the fitted class probability matrix.

LLKSeries

Single-column matrix of observed-data log-likelihoods, one row per EM iteration. Useful for diagnosing convergence.

converged

Logical. TRUE if the EM loop exited before maxIter iterations or if the final log-likelihood change was below tol.

n_obs

Integer. Number of observations used in estimation after listwise deletion on covariates.

Examples

# \donttest{
d  <- generate_data(200, "high", "covariate", seed = 1)
s1 <- lca_step1(d, Y.names = paste0("Y", 1:6), n_classes = 3)

# Estimate two-step gamma with mPhi fixed at Step-1 values
fZ <- fitZ_from_fit0(
  fit0     = s1$fit0,
  data     = d,
  Y.names  = paste0("Y", 1:6),
  Zp.names = "Zp",
  verbose  = TRUE
)
#> fitZ EM converged in 9 iterations.
fZ$mGamma   # Q x (T-1) coefficient matrix
#>                  C2         C3
#> Intercept  1.988800 -3.1317130
#> Zp        -1.017498  0.9190021
fZ$converged
#> [1] TRUE
# }