stepwiseIt: Stepwise item elimination

Description Usage Arguments Details Value See Also Examples

Description

This function eliminates items stepwise according to one of the following criteria: itemfit, Wald test, Andersen's LR-test

Usage

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## S3 method for class 'eRm'
stepwiseIt(object, criterion = list("itemfit"), alpha = 0.05,
           verbose = TRUE, maxstep = NA)

Arguments

object

Object of class eRm.

criterion

List with either "itemfit", "Waldtest" or "LRtest" as first element. Optionally, for the Waldtest and LRtest a second element containing the split criterion can be specified (see details).

alpha

Significance level.

verbose

If TRUE intermediate results are printed out.

maxstep

Maximum number of elimination steps. If NA the procedure stops when the itemset is Rasch homogeneous.

Details

If criterion = list("itemfit") the elimination stops when none of the p-values in itemfit is significant. Within each step the item with the largest chi-squared itemfit value is excluded.

If criterion = list("Waldtest") the elimination stops when none of the p-values resulting from the Wald test is significant. Within each step the item with the largest z-value in Wald test is excluded.

If criterion = list("LRtest") the elimination stops when Andersen's LR-test is not significant. Within each step the item with the largest z-value in Wald test is excluded.

Value

The function returns an object of class step containing:

X

Reduced data matrix (bad items eliminated)

fit

Object of class eRm with the final item parameter elimination

it.elim

Vector contaning the names of the eliminated items

res.wald

Elimination results for Wald test criterion

res.itemfit

Elimination results for itemfit criterion

res.LR

Elimination results for LR-test criterion

nsteps

Number of elimination steps

See Also

LRtest.Rm, Waldtest.Rm, itemfit.ppar

Examples

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## 2pl-data, 100 persons, 10 items
set.seed(123)
X <- sim.2pl(500, 10, 0.4)
res <- RM(X)

## elimination according to itemfit
stepwiseIt(res, criterion = list("itemfit"))      

## Wald test based on mean splitting
stepwiseIt(res, criterion = list("Waldtest","mean")) 

## Andersen LR-test based on random split
set.seed(123)
groupvec <- sample(1:3, 500, replace = TRUE)
stepwiseIt(res, criterion = list("LRtest",groupvec))


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