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CDM

IRT.frequencies

S3 Method for Computing Observed and Expected Frequencies of Univariate and Bivariate Marginals

Description

This S3 method computes observed and expected frequencies for univariate and bivariate distributions.

Usage

IRT.frequencies(object, ...)

IRT_frequencies_default(data, post, probs, weights=NULL)

IRT_frequencies_wrapper(object, ...)

## S3 method for class 'din'
IRT.frequencies(object, ...)

## S3 method for class 'gdina'
IRT.frequencies(object, ...)

## S3 method for class 'mcdina'
IRT.frequencies(object, ...)

## S3 method for class 'gdm'
IRT.frequencies(object, ...)

## S3 method for class 'slca'
IRT.frequencies(object, ...)

Arguments

`object`	Object of classes `din`, `gdina`, `mcdina`, `gdm` or `slca`.
`...`	More arguments to be passed.
`data`	Item response data as extracted by `IRT.data`
`post`	Individual posterior distribution as extracted by `IRT.posterior`
`probs`	Individual posterior distribution as extracted by `IRT.irfprob`
`weights`	Optional vector of weights as included as the attribute `weights` in `IRT.data`

Value

List with following entries

`uni_obs`	Univariate observed distribution
`uni_exp`	Univariate expected distribution
`M_obs`	Univariate observed means
`M_exp`	Univariate expected means
`SD_obs`	Univariate observed standard deviations
`SD_exp`	Univariate expected standard deviations
`biv_obs`	Bivariate observed frequencies
`biv_exp`	Bivariate expected frequencies
`biv_N`	Bivariate sample size
`cov_obs`	Observed covariances
`cov_cor`	Expected covariances
`cor_obs`	Observed correlations
`cor_exp`	Expected correlations
`chisq`	Chi square statistic of local independence

Examples

## Not run: 
#############################################################################
# EXAMPLE 1: Usage IRT.frequencies
#############################################################################

data(sim.dina, package="CDM")
data(sim.qmatrix, package="CDM")

# estimate GDINA model
mod1 <- CDM::gdina( data=sim.dina,  q.matrix=sim.qmatrix)
summary(mod1)

# direct usage of IRT.frequencies
fres1 <- CDM::IRT.frequencies(mod1)

# use of the default function with input data
data <- CDM::IRT.data(object)
post <- CDM::IRT.posterior(object)
probs <- CDM::IRT.irfprob(object)
fres2 <- CDM::IRT_frequencies_default(data=data, post=post, probs=probs)

## End(Not run)

CDM

Cognitive Diagnosis Modeling

v7.5-15

GPL (>= 2)

Authors

Alexander Robitzsch [aut, cre], Thomas Kiefer [aut], Ann Cathrice George [aut], Ali Uenlue [aut]

Initial release

2020-03-10 14:19:21