mcmh_mc: MCMH Parameter Estimates for Multiple Chains

In mlthom/CogIRT: Cognitive Testing Using Item Response Theory

MCMH Parameter Estimates for Multiple Chains

Description

This function calculates MCMH parameter estimates for multiple chains. See documentation for mcmh_sc.R for more information.

Usage

mcmh_mc(
  chains = NULL,
  y = y,
  obj_fun = NULL,
  est_omega = TRUE,
  est_nu = TRUE,
  est_zeta = TRUE,
  lambda = NULL,
  kappa = NULL,
  gamma = NULL,
  omega0 = NULL,
  nu0 = NULL,
  zeta0 = NULL,
  omega_mu = NULL,
  omega_sigma2 = NULL,
  nu_mu = NULL,
  nu_sigma2 = NULL,
  zeta_mu = NULL,
  zeta_sigma2 = NULL,
  burn = NULL,
  thin = NULL,
  min_tune = NULL,
  tune_int = NULL,
  max_tune = NULL,
  niter = NULL
)

Arguments

chains	Number of chains in the MCMH sampler (scalar).
y	Matrix of item responses (K by IJ).
obj_fun	A function that calculates predictions and log-likelihood values for the selected model (character).
est_omega	Determines whether omega is estimated (logical).
est_nu	Determines whether nu is estimated (logical).
est_zeta	Determines whether zeta is estimated (logical).
lambda	Matrix of item structure parameters (IJ by JM).
kappa	Matrix of item guessing parameters (K by IJ).
gamma	Matrix of experimental structure parameters (JM by MN).
omega0	Starting values for omega.
nu0	Starting values for nu.
zeta0	Starting values for zeta.
omega_mu	Vector of means prior for omega (1 by MN).
nu_mu	Prior mean for nu (scalar).
zeta_mu	Vector of means prior for zeta (1 by JM).
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	MCMH tuning interval (scalar).
max_tune	Determines when tunning ends (scalar).
niter	Number of iterations of the MCMH sampler.
omega_sigma	Covariance matrix prior for omega (MN by MN).
zeta_sigma@	Covariance matrix prior for zeta (JM by JM).
nu_sigma@	Prior variance for nu (scalar).

Value

List with elements omega_draws (draws from every saved iteration of the MCMH 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).

Examples

mcmh_mc(chains = 3, y = sdirt$y, obj_fun = dich_response_model, est_omega = TRUE,
    est_nu = TRUE, est_zeta = TRUE, lambda = sdirt$lambda, kappa = sdirt$kappa,
    gamma = sdirt$gamma, omega0 = array(data = 0, dim = dim(sdirt$omega)),
    nu0 = array(data = 0, dim = c(ncol(sdirt$nu), 1)),
    zeta0 = array(data = 0, dim = dim(sdirt$zeta)),
    omega_mu = sdirt$omega_mu, omega_sigma2 = sdirt$omega_sigma2,
    nu_mu = matrix(sdirt$nu_mu), nu_sigma2 = matrix(sdirt$nu_sigma2),
    zeta_mu = sdirt$zeta_mu, zeta_sigma2 = sdirt$zeta_sigma2,
    burn = 0, thin = 10, min_tune = 50, tune_int = 50, max_tune = 1000,
    niter = 2000)

mlthom/CogIRT documentation built on June 13, 2022, 7:45 a.m.