TAM: designMatrices – R documentation

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TAM

designMatrices

Generation of Design Matrices

Description

Generate design matrices, and display them at console.

Usage

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

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)
designMatrices.mfr2(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"), progress=FALSE, maxKi=NULL)
rownames.design(X)

.A.PCM2( resp, Kitem=NULL, constraint="cases", Q=NULL)
   # generates ConQuest parametrization of partial credit model

.A.PCM3( resp, Kitem=NULL ) # 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`	A vector containing the maximum score 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 ξ.
`B`	The scoring matrix of item categories on θ 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 used
`X`	Object generated by `designMatrices`. This argument is used in `print.designMatrices` and `rownames.design`.
`formulaA`	An R formula object for generating the `A` design matrix. Variables in `formulaA` have to be included in `facets`.
`facets`	A data frame with observed facets. The number of rows must be equal to the number of rows in `resp`.
`constraint`	Constraint in estimation: `cases` assumes zero means of trait distributions and `items` a sum constraint of zero of item parameters
`Kitem`	Maximum number of categories per item
`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).

Note

The function designMatrices.mfr2 handles multi-faceted design for items with differing number of response options.

References

Andrich, D. (1982). An extension of the Rasch model for ratings providing both location and dispersion parameters. Psychometrika, 47(1), 105-113. doi: 10.1007/BF02293856

Examples

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

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

TAM

Test Analysis Modules

v3.6-45

GPL (>= 2)

Authors

Alexander Robitzsch [aut,cre] (<https://orcid.org/0000-0002-8226-3132>), Thomas Kiefer [aut], Margaret Wu [aut]

Initial release

2021-04-22 14:35:52