stepwiseIt function

Stepwise item elimination

This function eliminates items stepwise according to one of the following criteria: itemfit, Wald test, Andersen's LR-test UTF-8

## 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.

Returns

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

## 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))