mhmc_mc: 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
)

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).

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).