immer_ccml | R Documentation |
Estimates the partial credit model with a design matrix for item parameters with composite conditional maximum likelihood estimation. The estimation uses pairs of items X_i and X_j and considers conditional likelihoods P(X_i=k, X_j=h | θ) / P( X_i + X_j=k+h| θ ). By using this strategy, the trait θ cancels out (like in conditional maximum likelihood estimation). The proposed strategy is a generalization of the Zwinderman (1995) composite conditional maximum likelihood approach of the Rasch model to the partial credit model. See Varin, Reid and Firth (2011) for a general introduction to composite conditional maximum likelihood estimation.
immer_ccml( dat, weights=NULL, irtmodel="PCM", A=NULL, b_fixed=NULL, control=NULL ) ## S3 method for class 'immer_ccml' summary(object, digits=3, file=NULL, ...) ## S3 method for class 'immer_ccml' coef(object, ...) ## S3 method for class 'immer_ccml' vcov(object, ...)
dat |
Data frame with polytomous item responses 0,1,…, K |
weights |
Optional vector of sampling weights |
irtmodel |
Model string for specifying the item response model |
A |
Design matrix (items \times categories \times basis parameters). Entries for categories are for 1,…,K |
b_fixed |
Matrix with fixed b parameters |
control |
Control arguments for optimization function
|
object |
Object of class |
digits |
Number of digits after decimal to print |
file |
Name of a file in which the output should be sunk |
... |
Further arguments to be passed. |
The function estimates the partial credit model as
P(X_i=h | θ ) \propto \exp( h θ - b_{ih} ) with
b_{ih}=∑_l a_{ihl} ξ_l where the values a_{ihl}
are included in the design matrix A
and ξ_l denotes
basis item parameters.
List with following entries (selection)
coef |
Item parameters |
vcov |
Covariance matrix for item parameters |
se |
Standard errors for item parameters |
nlminb_result |
Output from optimization with
|
suff_stat |
Used sufficient statistics |
ic |
Information criteria |
Varin, C., Reid, N., & Firth, D. (2011). An overview of composite likelihood methods. Statistica Sinica, 21, 5-42.
Zwinderman, A. H. (1995). Pairwise parameter estimation in Rasch models. Applied Psychological Measurement, 19(4), 369-375.
See sirt::rasch.pairwise.itemcluster
of an implementation of the composite conditional maximum likelihood approach for the
Rasch model.
############################################################################# # EXAMPLE 1: Partial credit model with CCML estimation ############################################################################# library(TAM) data(data.gpcm, package="TAM") dat <- data.gpcm #-- initial MML estimation in TAM to create a design matrix mod1a <- TAM::tam.mml(dat, irtmodel="PCM2") summary(mod1a) #* define design matrix A <- - mod1a$A[,-1,-1] A <- A[,,-1] str(A) #-- estimate model mod1b <- immer::immer_ccml( dat, A=A) summary(mod1b)
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