IRT.marginal_posterior {CDM} R Documentation

## S3 Method for Computation of Marginal Posterior Distribution

### Description

Computes marginal posterior distributions for fitted models in the CDM package.

### Usage

IRT.marginal_posterior(object, dim, remove_zeroprobs=TRUE, ...)

## S3 method for class 'din'
IRT.marginal_posterior(object, dim, remove_zeroprobs=TRUE, ...)
## S3 method for class 'gdina'
IRT.marginal_posterior(object, dim, remove_zeroprobs=TRUE, ...)
## S3 method for class 'mcdina'
IRT.marginal_posterior(object, dim, remove_zeroprobs=TRUE, ...)



### Arguments

 object Object of class din, gdina, mcdina dim Numeric or character vector indicating dimensions of posterior distribution which should be marginalized remove_zeroprobs Logical indicating whether classes with zero probabilities should be removed ... Further arguments to be passed

### Value

List with entries

 marg_post Marginal posterior distribution map MAP estimate (individual classification) theta Skill classes

IRT.posterior

### Examples

## Not run:
#############################################################################
# EXAMPLE 1: Dataset with three hierarchical skills
#############################################################################

# simulated data with hierarchical skills:
# skill A with 4 levels, skill B with 2 levels and skill C with 3 levels

data(data.cdm10, package="CDM"")
dat <- data.cdm10$data Q <- data.cdm10$q.matrix
print(Q)

# define hierarchical skill structure
B <- "A1 > A2 > A3
C1 > C2"
skill_space <- CDM::skillspace.hierarchy(B=B, skill.names=colnames(Q))
zeroprob.skillclasses <- skill_space$zeroprob.skillclasses # estimate DINA model mod1 <- CDM::gdina( dat, q.matrix=Q, zeroprob.skillclasses=zeroprob.skillclasses, rule="DINA") summary(mod1) # classification for skill A res <- CDM::IRT.marginal_posterior(object=mod1, dim=c("A1","A2","A3") ) table(res$map)

# classification for skill B
res <- CDM::IRT.marginal_posterior(object=mod1, dim=c("B") )
table(res$map) # classification for skill C res <- CDM::IRT.marginal_posterior(object=mod1, dim=c("C1","C2") ) table(res$map)

## End(Not run)


[Package CDM version 8.2-6 Index]