designMatrices.Rd
Generate design matrices, and display them at console.
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
modeltype | Type of item response model. Until now, the
partial credit model ( |
---|---|
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 \(\xi\). |
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 used |
X | Object generated by |
formulaA | An R formula object for generating the |
facets | A data frame with observed facets. The number of rows must be equal
to the number of rows in |
constraint | Constraint in estimation: |
Kitem | Maximum number of categories per item |
progress | Display progress for creation of design matrices |
... | Further arguments |
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).
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
The function designMatrices.mfr2
handles multi-faceted design for
items with differing number of response options.
See data.sim.mfr
for some examples for creating design matrices.
########################################################### # 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