Waldtest {eRm} | R Documentation |
Performs a Wald test on item-level by splitting subjects into subgroups.
## S3 method for class 'Rm': Waldtest(object, splitcr = "median") ## S3 method for class 'wald': print(x,...)
object |
Object of class RM . |
splitcr |
Split criterion for subject raw score splitting. median
uses the median as split criterion, mean performs a mean-split.
Optionally splitcr can also be a dichotomous vector which assigns each person to a
certain subgroup (e.g., following an external criterion). |
x |
Object of class wald . |
... |
Further arguments passed to or from other methods. They are ignored in this function. |
The Wald test only works if both subgroups have the same parameters. For instance, for small samples in RSM or PCM the user must find an appropriate split such that in each subgroup the same item-category parameters occur.
Returns an object of class wald
containing:
coef.table |
Data frame with test statistics, z- and p-values. |
etapar1 |
Eta parameters for first subgroup |
se1 |
Standard errors for first subgroup |
etapar2 |
Eta parameters for second subgroup |
se2 |
Standard errors for second subgroup |
Patrick Mair, Reinhold Hatzinger
Fischer, G. H., and Molenaar, I. (1995). Rasch Models - Foundations, Recent Developements, and Applications. Springer.
Fischer, G. H., and Scheiblechner, H. (1970). Algorithmen und Programme für das probabilistische Testmodell von Rasch [Algorithms and programs for Rasch's probabilistic test model]. Psychologische Beiträge, 12, 23-51.
#Wald test for dichotomous Rasch model with median subject split data(raschdat1) res <- RM(raschdat1) Waldtest(res) #Wald test with user-defined subject split splitvec <- sample(1:2,100,replace=TRUE) Waldtest(res, splitcr = splitvec)