gpcmCat {catSurv}

Computerized Adaptive Testing Generalized Partial Credit Model

Description

This function fits the generalized partial credit model for ordinal polytomous data and populates the fitted values for discrimination and difficulty parameters to an object of class Cat.

Usage

## S4 method for signature 'data.frame'
gpcmCat(data, quadraturePoints = 21, ...)

## S4 method for signature 'gpcm'
gpcmCat(data, quadraturePoints = NULL, ...)

Arguments

data

A data frame of manifest variables or an object of class gpcm.

quadraturePoints

A numeric to be passed into the gpcm function indicating the number of Gauss-Hermite quadrature points. Only applicable when data is a data frame. Default value is 21.

...

arguments to be passed to methods. For more details about the arguments, see gpcm in the ltm package.

Details

The data argument of the function gpcmCat is either a data frame or an object of class gpcm from the ltm package. If it is a data frame each row represents a respondent and each column represents a question item. If it is an object of the class gpcm, it is output from the gpcm function in the ltm package.

The quadraturePoints argument of the function gpcmCat is used only when the data argument is a data frame. quadraturePoints is then passed to the gpcm function from the ltm package when fitting the generalized partial credit model to the data and is used when approximating the value of integrals.

Value

The function gpcmCat returns an object of class Cat with changes to the following slots:

• difficulty A list of difficulty parameters, where each element in the list corresponds to the difficulty parameters for an item.

• discrimination A vector consisting of of discrimination parameters for each item.

• model The string "gpcm", indicating this Cat object corresponds to a generalized partial credit model.

See Cat-class for default values of Cat object slots. See Examples and setters for example code to change slot values.

Note

This Cat object should be used for testing package functionality only.

In case the Hessian matrix at convergence is not positive definite try to use start.val = "random".

Author(s)

Haley Acevedo, Ryden Butler, Josh W. Cutler, Matt Malis, Jacob M. Montgomery, Tom Wilkinson, Erin Rossiter, Min Hee Seo, Alex Weil

References

Baker, Frank B. and Seock-Ho Kim. 2004. Item Response Theory: Parameter Estimation Techniques. New York: Marcel Dekker.

Muraki, Eiji. 1992. “A generalized partial credit model: Application of an EM algorithm." ETS Research Report Series 1992(1):1-30.

Rizopoulos, Dimitris. 2006. “ltm: An R Package for Latent Variable Modeling and Item Response Theory Analyses." Journal of Statistical Software 17(5):1-25.

See Also

Cat-class, grmCat, polknowTAPS, probability

Examples

## Not run: 
## Creating Cat object with fitted object of class gpcm
data(polknowTAPS)
gpcm_fit <- gpcm(polknowTAPS, constraint = "gpcm", control = list(iter.qN = 200, GHk = 100))
class(gpcm_fit)
gpcm_cat <- gpcmCat(gpcm_fit)

## End(Not run)

## Creating Cat objects from large datasets is computationally expensive
## Load the Cat object created from the above code
data(gpcm_cat)

## Slots that have changed from default values
getModel(gpcm_cat)
getDifficulty(gpcm_cat)
getDiscrimination(gpcm_cat)

## Changing slots from default values
setEstimation(gpcm_cat) <- "MLE"
setSelection(gpcm_cat) <- "MFI"