mhmc_mc: MHMC Parameter Estimates for Multiple Chains

In cogirt: Cognitive Testing Using Item Response Theory

MHMC Parameter Estimates for Multiple Chains

Description

This function calculates MHMC parameter estimates for multiple chains. See documentation for mhmc_sc.R for more information.

Usage

mhmc_mc(
  chains = NULL,
  y = y,
  obj_fun = NULL,
  link = NULL,
  est_omega = TRUE,
  est_lambda = TRUE,
  est_zeta = TRUE,
  est_nu = TRUE,
  omega0 = NULL,
  gamma0 = NULL,
  lambda0 = NULL,
  zeta0 = NULL,
  nu0 = NULL,
  kappa0 = NULL,
  omega_mu = NULL,
  omega_sigma2 = NULL,
  lambda_mu = NULL,
  lambda_sigma2 = NULL,
  zeta_mu = NULL,
  zeta_sigma2 = NULL,
  nu_mu = NULL,
  nu_sigma2 = NULL,
  burn = NULL,
  thin = NULL,
  min_tune = NULL,
  tune_int = NULL,
  max_tune = NULL,
  niter = NULL,
  psrf = FALSE
)

Arguments

chains	Number of chains in the MHMC sampler (scalar).
y	Item response matrix (K by IJ).
obj_fun	A function that calculates predictions and log-likelihood values for the selected model (character).
link	Choose between "logit" or "probit" link functions.
est_omega	Determines whether omega is estimated (logical).
est_lambda	Determines whether nu is estimated (logical).
est_zeta	Determines whether zeta is estimated (logical).
est_nu	Determines whether nu is estimated (logical).
omega0	Starting or known values for omega (K by MN).
gamma0	Starting or known values for gamma (JM by MN).
lambda0	Starting or known values for lambda (IJ by JM).
zeta0	Starting or known values for zeta (K by JM).
nu0	Starting or known values for nu (IJ by 1).
kappa0	Starting or known values for kappa (1 by IJ).
omega_mu	Mean prior for omega (1 by MN).
omega_sigma2	Covariance prior for omega (MN by MN).
lambda_mu	Mean prior for lambda (1 by JM)
lambda_sigma2	Covariance prior for lambda (JM by JM)
zeta_mu	Mean prior for zeta (1 by JM).
zeta_sigma2	Covariance prior for zeta (JM by JM).
nu_mu	Mean prior for nu (1 by 1)
nu_sigma2	Covariance prior for nu (1 by 1)
burn	Number of iterations at the beginning of an MCMC run to discard (scalar).
thin	Determines every nth observation retained (scalar).
min_tune	Determines when tunning begins (scalar).
tune_int	MHMC tuning interval (scalar).
max_tune	Determines when tunning ends (scalar).
niter	Number of iterations of the MHMC sampler.
psrf	Estimate potential scale reduction factor (logical).

Value

List with elements omega_draws (draws from every saved iteration of the MHMC sampler), omegaEAP (expected a posteriori estimates for omega), omegaPSD (posterior standard deviation estimates for omega), omega_psrf (potential scale reduction factor for omega), nuEAP (expected a posteriori estimates for nu), nuPSD (posterior standard deviation estimates for nu), nu_psrf (potential scale reduction factor for nu), zetaEAP (expected a posteriori estimates for zeta), zetaPSD (posterior standard deviation estimates for zeta), zeta_psrf (potential scale reduction factor for zeta).

cogirt documentation built on April 3, 2025, 8:14 p.m.