trait.posterior: Calculate Posterior Estimates for Trait Parameters

In GPCMlasso: Regularized Explanatory Generalized Partial Credit Models

Calculate Posterior Estimates for Trait Parameters

Description

Calculates posterior estimates for trait/person parameters for a fitted GPCMlasso model using the assumed Gaussian distribution of the person parameters.

Usage

trait.posterior(model, coefs = NULL, cores = 25, tol = 1e-04)

Arguments

model	Object of class GPCMlasso.
coefs	Optional vector of coefficients. If coefs = NULL, the coefficients from the BIC-optimal model are used, or, if cross-validation was performed, the coefficients from the cross-validation optimal model are used.
cores	Number of cores used for parallel computation.
tol	Deprecated. Kept for backward compatibility.

Details

The function computes posterior means of the latent trait parameters by Gauss-Hermite quadrature. If no coefficient vector is supplied, the cross-validation optimal coefficient vector is used when cross-validation was performed; otherwise, the BIC-optimal coefficient vector is used.

Value

Numeric vector containing posterior estimates of the trait/person parameters.

Author(s)

Gunther Schauberger
[e-mail]

See Also

GPCMlasso, predict.GPCMlasso

Examples

data(tenseness_small)

form0 <- as.formula(
  paste(
    "cbind(",
    paste(colnames(tenseness_small)[1:5], collapse = ","),
    ") ~ 0"
  )
)

## Not run: 
rsm0 <- GPCMlasso(
  formula = form0,
  data = tenseness_small,
  model = "RSM",
  control = ctrl_GPCMlasso(cores = 1, trace = FALSE)
)

theta_hat <- trait.posterior(rsm0, cores = 1)
summary(theta_hat)

## End(Not run)

GPCMlasso documentation built on Sept. 8, 2026, 5:08 p.m.