Prediction from fitted Item focussed Trees

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Description

The function returns predictions of item parameters obtained by item focussed recursive partitioning based on the Rasch Model or the Logistic Regression Approach for DIF detection.

Usage

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## S3 method for class 'DIFtree'
predict(object, item, newdata, ...)

Arguments

object

Object of class DIFtree

item

Number of the item, for which the prediction shall be returned

newdata

New data.frame, for which the prediction shall be returned

...

Further arguments passed to or from other methods

Details

For "Rasch" model the function returns the predicted item difficulty. For "Logistic" models the function returns the predicted intercept and/or slope.

Author(s)

Moritz Berger <moritz.berger@stat.uni-muenchen.de>
http://www.statistik.lmu.de/~mberger/

References

Berger, Moritz and Tutz, Gerhard (2015): Detection of Uniform and Non-Uniform Differential Item Functioning by Item Focussed Trees, Cornell University Library, arXiv:1511.07178

Tutz, Gerhard and Berger, Moritz (2015): Item Focused Trees for the Identification of Items in Differential Item Functioning, Psychometrika, published online, DOI: 10.1007/s11336-015-9488-3

See Also

DIFtree, plot.DIFtree, summary.DIFtree

Examples

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data(data_sim)
 
Y <- data_sim[,1]
X <- data_sim[,-1]

Xnew <- data.frame("x1"=c(0,1),"x2"=c(-1.1,2.5),"x3"=c(1,0),"x4"=c(-0.2,0.7))
 
## Not run: 
 
mod <- DIFtree(Y=Y,X=X,model="Logistic",type="udif",alpha=0.05,nperm=1000,trace=TRUE)
 
predict(mod,item=1,Xnew)

## End(Not run)