mcmc: Performs MCMC routine for DCM

Description Usage Arguments Value

View source: R/dcm_mcmc_scorer.R

Description

Performs MCMC routine for DCM

Usage

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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.