mhrm: MHRM Parameter Estimates for Multiple Chains

In cogirt: Cognitive Testing Using Item Response Theory

MHRM Parameter Estimates for Multiple Chains

Description

This function calculates mhrm parameter estimates for multiple chains.

Usage

mhrm(
  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,
  constraints = NULL,
  J = NULL,
  M = NULL,
  N = NULL,
  verbose = TRUE,
  ...
)

Arguments

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 lambda is estimated (logical).
est_zeta	Determines whether zeta is estimated (logical).
est_nu	Determines whether nu is estimated (logical).
omega0	Starting values for omega.
gamma0	Either a matrix of contrast codes (JM by MN) or the name in quotes of the desired R stats contrast function (i.e., "contr.helmert", "contr.poly", "contr.sum", "contr.treatment", or "contr.SAS"). If using the R stats contrast function the user must also specify J, M, and N, as well as ensure that items in y are arranged so that the first set of I items correspond to the first level if the contrast, the next set of I items correspond to the second level of the contrast, etc. For example, in an experiment with two conditions (i.e., J = 2) where the user requests two contrasts (i.e., N = 2) from the "contr.treatment" function, the first set of I items will all receive a contrast code of 0 and the second set of I items will all receive a contrast code of 1. In an experiment with three conditions (i.e., J = 3) where the user requests three contrasts (i.e., N = 3) from the "contr.poly" function, first set of I items will receive the lowest value code for linear and quadratic contrasts, the second set of I items will all receive the middle value code for linear and quadratic contrasts, and the last set of I items will all receive the highest value code for linear and quadratic contrasts.
lambda0	Item slope matrix (IJ by JM).
zeta0	Starting values for zeta.
nu0	Starting values for nu (IJ by 1).
kappa0	Item guessing matrix (IJ by 1).
omega_mu	Vector of means prior for omega (1 by MN).
omega_sigma2	Covariance matrix 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	Vector of means prior for zeta (1 by JM).
zeta_sigma2	Covariance matrix prior for zeta (JM by JM).
nu_mu	Prior mean for nu (scalar).
nu_sigma2	Prior variance for nu (scalar).
constraints	Item parameter constraints.
J	Number of conditions (required if using the R stats contrast function).
M	Number of ability (or trait) dimensions (required if using the R stats contrast function).
N	Number of contrasts including intercept (required if using the R stats contrast function).
verbose	Logical (TRUE or FALSE) indicating whether to print progress.
...	Additional arguments.

Value

List with elements for all parameters estimated, information values for all parameters estimated, and the model log-likelihood value.

References

Cai, L. (2010). High-dimensional exploratory item factor analysis by a Metropolis-Hastings Robbins-Monro algorithm. Psychometrika, 75(1), 33-57.

Cai, L. (2010). Metropolis-Hastings Robbins-Monro algorithm for confirmatory item factor analysis. Journal of Educational and Behavioral Statistics, 35(3), 307-335.

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