# IRT.likelihood: S3 Methods for Extracting of the Individual Likelihood and... In CDM: Cognitive Diagnosis Modeling

## Description

Functions for extracting the individual likelihood and individual posterior distribution.

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33``` ```IRT.likelihood(object, ...) IRT.posterior(object, ...) ## S3 method for class 'din' IRT.likelihood(object, ...) ## S3 method for class 'din' IRT.posterior(object, ...) ## S3 method for class 'gdina' IRT.likelihood(object, ...) ## S3 method for class 'gdina' IRT.posterior(object, ...) ## S3 method for class 'gdm' IRT.likelihood(object, ...) ## S3 method for class 'gdm' IRT.posterior(object, ...) ## S3 method for class 'mcdina' IRT.likelihood(object, ...) ## S3 method for class 'mcdina' IRT.posterior(object, ...) ## S3 method for class 'reglca' IRT.likelihood(object, ...) ## S3 method for class 'reglca' IRT.posterior(object, ...) ## S3 method for class 'slca' IRT.likelihood(object, ...) ## S3 method for class 'slca' IRT.posterior(object, ...) ```

## Arguments

 `object` Object of classes `din`, `gdina`, `mcdina`, `gdm`, `slca`, `reglca`. `...` More arguments to be passed.

## Value

For both functions `IRT.likelihood` and `IRT.posterior`, it is a matrix with attributes

 `theta` Uni- or multidimensional skill space (theta grid in item response models). `prob.theta` Probability distribution of `theta` `skillspace` Design matrix and estimated parameters for skill space distribution (only for `IRT.posterior.slca`) `G` Number of groups

`GDINA::indlogLik`, `GDINA::indlogPost`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17``` ```############################################################################# # EXAMPLE 1: Extracting likelihood and posterior from a DINA model ############################################################################# data(sim.dina, package="CDM") data(sim.qmatrix, package="CDM") #*** estimate model mod1 <- CDM::din( sim.dina, q.matrix=sim.qmatrix, rule="DINA") #*** extract likelihood likemod1 <- CDM::IRT.likelihood(mod1) str(likemod1) # extract theta attr(likemod1, "theta" ) #*** extract posterior pomod1 <- CDM::IRT.posterior( mod1 ) str(pomod1) ```