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