lsirm12pl (version 2.0.0)

diagnostic: 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
)

Value

diagnostic returns plots for checking MCMC convergence for selected parameters.

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", "theta_sd", "z", "w", 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. For the keys "z" and "w", the value is a matrix with two columns: the first column represents the indices of respondents/items, and the second column represents the dimension indices.

gelman.diag

Logical; If TRUE, the Gelman-Rubin convergence diagnostic will be printed. Default is FALSE.

Examples

# \donttest{
# 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)

# For specific latent positions
diagnostic(lsirm_result, draw.item = list(z = matrix(c(1,1, 2,1, 1,2), ncol=2, byrow=TRUE),
                                       w = matrix(c(1,1, 2,1), ncol=2, byrow=TRUE)))

# }