diagnostic: Diagnostic the result of LSIRM.

In lsirm12pl: Latent Space Item Response Model

Diagnostic the result of LSIRM.

Description

diagnostic checks the convergence of MCMC for LSIRM parameters using various diagnostic tools, such as trace plots, posterior density distributions, autocorrelation functions (ACF), and Gelman-Rubin-Brooks plots.

Usage

diagnostic(
  object,
  draw.item = list(beta = c(1), theta = c(1)),
  gelman.diag = FALSE
)

Arguments

object	Object of class lsirm.
draw.item	List; Each key in the list corresponds to a specific parameters such as "beta", "theta", "gamma", "alpha", "sigma", "sigma_sd", and "zw.dist". The values of the list indicate the indices of these parameters. For the key "zw.dist", the value is a matrix with two columns: the first column represents the indices of respondents, and the second column represents the indices of items.
gelman.diag	Logical; If TRUE, the Gelman-Rubin convergence diagnostic will be printed. Default is FALSE.

Value

diagnostic returns plots for checking MCMC convergence for selected parameters.

Examples

# Generate example item response matrix
data     <- matrix(rbinom(500, size = 1, prob = 0.5), ncol=10, nrow=50)

# For 1PL LSIRM
lsirm_result <- lsirm(data ~ lsirm1pl(spikenslab = FALSE, fixed_gamma = FALSE))
diagnostic(lsirm_result)

# For 2PL LSIRM
lsirm_result <- lsirm(data ~ lsirm2pl(spikenslab = FALSE, fixed_gamma = FALSE))
diagnostic(lsirm_result)

lsirm12pl documentation built on April 4, 2025, 2:40 a.m.