eRm: stepwiseIt – R documentation

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stepwiseIt

Stepwise item elimination

Description

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

Usage

## 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

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))

eRm

Extended Rasch Modeling

v1.0-2

GPL-3

Authors

Patrick Mair [cre, aut], Reinhold Hatzinger [aut], Marco J. Maier [aut], Thomas Rusch [ctb], Rudolf Debelak [ctb]

Initial release

2021-02-11