stepwiseIt | R Documentation |
This function eliminates items stepwise according to one of the following criteria: itemfit, Wald test, Andersen's LR-test
## S3 method for class 'eRm'
stepwiseIt(object, criterion = list("itemfit"), alpha = 0.05,
verbose = TRUE, maxstep = NA)
object |
Object of class |
criterion |
List with either |
alpha |
Significance level. |
verbose |
If |
maxstep |
Maximum number of elimination steps. If |
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.
The function returns an object of class step
containing:
X |
Reduced data matrix (bad items eliminated) |
fit |
Object of class |
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 |
LRtest.Rm
, Waldtest.Rm
, itemfit.ppar
## 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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