mcmc: Performs MCMC routine for DCM

In dcmr: Attribute profile estimation using Diagnostic Classification Models and MCMC

Description

Performs MCMC routine for DCM

Usage

mcmc(observations, nattributes, qmatrix, pmatrix, parameter.means,
  parameter.acov, nobservations, nreps, initial.class, nchains,
  threshold.labels, lambda.equations, is.pi.r, is.parameter.randomized,
  parameterization.method, percent.reps.to.discard)

Arguments

observations	a data frame or matrix of dichotomous responses
nattributes	numeric value of number of attributes
qmatrix	a data frame or matrix of 1s and 0s indicating relation between items and attributes. This matrix specifies which items are required for mastery of each attribute (i.e., latent variable). A matrix must be a size of nItems X nAttributes
pmatrix	a numeric nclasses by nattributes matrix of all possible attribute profiles
parameter.means	a numerical vector of calibrated item and structural parameters
parameter.acov	a numerical matrix of covariances of item and structural parameters
nobservations	a numeric value indicating number of rows of the observation data frame or matrix
nreps	The number of iterations in MCMC per chain
initial.class	The initial value of attribute profile for each respondent
nchains	The number of chains in MCMC
threshold.labels	an nclasses by nitems character matrix with appropriate item threshold labels
lambda.equations	lambda parameter equations
is.pi.r	If FALSE (the default), parameter values are the type of taus and nus or lambdas and gammas else they are the type pis and rs as used in NC-RUM parameterization
is.parameter.randomized	if true parameter estimates are randomized using acov matrix
parameterization.method	optional character string of parameterization method used to calibrate parameters
percent.reps.to.discard	The percent of iterations to be discarded

Value

a list of class and parameter data frame containing all accepted iteraction of MCMC

dcmr documentation built on May 29, 2017, 10:41 p.m.