DIFtree (version 3.1.6)

predict.DIFtree: Prediction from fitted Item focussed Trees

Description

The function returns predictions of item parameters obtained by item focussed recursive partitioning in dichotomous or polytomous items.

Usage

# S3 method for 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. For "PCM" the function returns the predicted threshold parameters.

References

Berger, Moritz and Tutz, Gerhard (2016): Detection of Uniform and Non-Uniform Differential Item Functioning by Item Focussed Trees, Journal of Educational and Behavioral Statistics 41(6), 559-592.

Bollmann, Stella, Berger, Moritz & Tutz, Gerhard (2018): Item-Focussed Trees for the Detection of Differential Item Functioning in Partial Credit Models, Educational and Psychological Measurement 78(5), 781-804.

Tutz, Gerhard and Berger, Moritz (2016): Item focussed Trees for the Identification of Items in Differential Item Functioning, Psychometrika 81(3), 727-750.

See Also

DIFtree, plot.DIFtree, summary.DIFtree

Examples

# NOT RUN {
data(data_sim_Rasch)
 
Y <- data_sim_Rasch[,1]
X <- data_sim_Rasch[,-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)
# }
# NOT RUN {
# }