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Estimate two-step covariate model via multilevLCA (optional reference path)

Source: R/lca_measurement.R
fitZ_from_multiLCA.Rd

Calls multilevLCA::multiLCA with fixedpars = 1 and Z = Zp.names to fit the two-step covariate model. This is the original multilevLCA approach and is used when get.twostep.vcov = TRUE in three_step() to obtain multilevLCA's corrected standard errors for the two-step gamma estimates.

Usage

fitZ_from_multiLCA(
  data,
  Y.names,
  n_classes,
  Zp.names,
  maxIter.measurement,
  measurement.tol,
  covariate.tol,
  iter.measurement,
  R2.threshold,
  incomplete = FALSE,
  rebase = "C1",
  verbose = FALSE
)

Arguments

data

A data.frame.

Y.names

Character vector of item column names.

n_classes

Integer. Number of latent classes.

Zp.names

Character vector of covariate column names.

maxIter.measurement

Maximum EM iterations.

measurement.tol

Convergence tolerance.

covariate.tol

NR tolerance for the covariate model.

iter.measurement

Number of random restarts.

R2.threshold

Entropy R\(^2\) restart threshold.

incomplete

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

rebase

Character or integer. Reference class for column naming of $mGamma. Must match the rebase used in three_step() so coefficient labels are consistent. Default "C1".

verbose

Logical.

Value

A list with the following elements:

mGamma

Q x (T-1) numeric matrix of multinomial logit coefficients. Rows are named by covariate (including "Intercept"), columns by non-reference class (e.g. "C2", "C3").

mPhi

Item parameter matrix (items x classes) from the fixed-parameter multilevLCA fit.

vOmega

Length-T vector of marginal class proportions, computed as the average of the fitted class probability matrix (vPi_avg in multilevLCA output).

LLKSeries

Matrix of observed-data log-likelihoods across EM iterations, passed through directly from the multilevLCA fit.

raw_fit

The full multilevLCA::multiLCA() output object, including $Varmat_cor (corrected variance matrix) and $SEs_cor_gamma (corrected standard errors for mGamma) if available.

Examples

# \donttest{
d <- generate_data(200, "high", "covariate", seed = 1)

# Two-step estimation via multiLCA (fixedpars = 1)
fZ_ml <- fitZ_from_multiLCA(
  data                = d,
  Y.names             = paste0("Y", 1:6),
  n_classes           = 3,
  Zp.names            = "Zp",
  maxIter.measurement = 5000L,
  measurement.tol     = 1e-8,
  covariate.tol       = 1e-6,
  iter.measurement    = 10L,
  R2.threshold        = 0.70
)
fZ_ml$mGamma           # two-step estimates
#>                  C2         C3
#> Intercept  1.990672 -3.1319910
#> Zp        -1.018352  0.9190157
fZ_ml$raw_fit$Varmat_cor   # multilevLCA corrected vcov
#>             [,1]         [,2]         [,3]         [,4]
#> [1,]  0.28553970 -0.119040291  0.051831951 -0.016507545
#> [2,] -0.11904029  0.065539163 -0.009799449  0.005370232
#> [3,]  0.05183195 -0.009799449  0.613479393 -0.157124127
#> [4,] -0.01650754  0.005370232 -0.157124127  0.043473866
# }