TAM (version 0.04-43)

designMatrices: Generation of Design Matrices

Description

Generate design Matrices, and display at console.

Usage

designMatrices(modeltype = c("PCM", "RSM"), maxKi = NULL, resp = resp,
    ndim = 1, A = NULL, B = NULL, Q = NULL, R = NULL, ...)
    
print.designMatrices(X, ...)

designMatrices.mfr(resp, formulaA = ~ item + item:step, facets = NULL, 
    constraint = c("cases", "items"), ndim = 1, Q=NULL, A=NULL, B=NULL ,
    progress=FALSE)
.A.matrix(resp, formulaA = ~ item + item*step, facets = NULL, 
    constraint = c("cases", "items") )
rownames.design(X)

.A.PCM2( resp )     # generates ConQuest parametrization of partial credit model

.A.PCM3( resp )     # parametrization for A matrix in the dispersion model

Arguments

modeltype

Type of item response model. Until now, the partial credit model (PCM; 'item+item*step') and the rating scale model (RSM; 'item+step') is implemented.

maxKi

Maximum category per item

resp

Data frame of item responses

ndim

Number of dimensions

A

The design matrix for linking item category parameters to generalized item parameters $\xi$.

B

The scoring matrix of item categories on $\theta$ dimensions.

Q

A loading matrix of items on dimensions with number of rows equal the number of items and the number of columns equals the number of dimensions in the item response model.

R

This argument is not yet used

X

Object generated by designMatrices. This argument is used in print.designMatrices and rownames.design.

formulaA

An Rformula object for generating the A design matrix

facets

A data frame with observed facets (DESCRIBE IT IN MORE DETAIL!)

constraint

Constraint in estimation: cases assumes zero means of trait distributions and items a sum constraint of zero of item parameters

progress

Display progress for creation of design matrices

...

Further arguments

Details

The function .A.PCM2 generates the Conquest parametrization of the partial credit model. The function .A.PCM3 generates the parametrization for the $A$ design matrix in the dispersion model for ordered data (Andrich, 1982).

References

Andrich, D. (1982). An extension of the Rasch model for ratings providing both location and dispersion parameters. Psychometrika, 47, 105-113.

See Also

See sim.mfr for some examples for creating design matrices.

Examples

###########################################################
# different parametrizations for ordered data
data( data.gpcm )
resp <- data.gpcm

# parametrization for partial credit model
A1 <- designMatrices( resp= resp )$A
# item difficulty and threshold parametrization
A2 <- .A.PCM2( resp )
# dispersion model of Andrich (1982)
A3 <- .A.PCM3( resp )
# rating scale model
A4 <- designMatrices( resp= resp , modeltype="RSM" )$A