Performs a Wald test on item-level by splitting subjects into subgroups.
Usage
# S3 method for Rm
Waldtest(object, splitcr = "median")
# S3 method for wald
print(x,...)
Value
Returns an object of class wald containing:
coef.table
Data frame with test statistics, z- and p-values.
betapar1
Beta parameters for first subgroup
se.beta1
Standard errors for first subgroup
betapar2
Beta parameters for second subgroup
se.beta2
Standard errors for second subgroup
se.beta2
Standard errors for second subgroup
spl.gr
Names and levels for splitcr.
call
The matched call.
Arguments
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). This vector can be numeric, character or a factor.
x
Object of class wald.
...
Further arguments passed to or from other methods. They are ignored in this function.
Author
Patrick Mair, Reinhold Hatzinger
Details
Items are eliminated if they not have the same number of categories in each subgroup.
To avoid this problem, for RSM and PCM it is considered to use a random or another user-defined split.
If the data set contains missing values and mean or median is specified as splitcriterion,
means or medians are calculated for each missing value subgroup and consequently used for raw score splitting.
References
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 fuer das
probabilistische Testmodell von Rasch [Algorithms and programs for Rasch's
probabilistic test model]. Psychologische Beitraege, 12, 23-51.
#Wald test for Rasch model with user-defined subject splitres <- RM(raschdat2)
splitvec <- sample(1:2,25,replace=TRUE)
Waldtest(res, splitcr = splitvec)