LRtest: Computation of Andersen's LR-test.

Description

This LR-test is based on subject subgroup splitting.

Usage

## S3 method for class 'Rm':
LRtest(object, splitcr = "median", se = FALSE)
## S3 method for class 'LR':
plotGOF(x, beta.subset = "all", xlab = "Beta Group 1", 
ylab = "Beta Group 2", ylim = c(-3, 3), xlim = c(-3, 3), type = "p", ...)
## S3 method for class 'LR':
print(x,...)
## S3 method for class 'LR':
summary(object,...)

Arguments

object

Object of class Rm.

splitcr

Split criterion for subject raw score splitting. all.r corresponds to a full raw score split, median uses the median as split criterion, mean performs a mean-split. Optionally splitcr can also be

If TRUE standard errors for beta's are computed.

Object of class LR for visualizing the fit of single items.

beta.subset

If "all", all items are plotted. Otherwise numeric subset vector can be specified.

xlab

Label on x-axis.

ylab

Label on y-axis.

xlim

Limits on x-axis.

ylim

Limits on y-axis.

type

Plotting type.

...

Additional graphical parameters.

Value

LRtest returns an object of class LR containing:
LRLR-value.
dfDegrees of freedom of the test statistic.
ChisqChi-square value with corresponding df.
pvalueP-value of the test.
likgroupLog-likelihood values for the subgroups
betalistList of beta parameters for the subgroups.
selistList of standard errors of beta's.
etalistList of eta parameters for the subgroups.

References

Fischer, G. H., and Molenaar, I. (1995). Rasch Models - Foundations, Recent Developements, and Applications. Springer. Mair, P., and Hatzinger, R. (2007). Extended Rasch modeling: The eRm package for the application of IRT models in R. Journal of Statistical Software, 20(9), 1-20. Mair, P., and Hatzinger, R. (2007). CML based estimation of extended Rasch models with the eRm package in R. Psychology Science, 49, 26-43.

Examples

Run this code

# LR-test on dichotomous Rasch model with user-defined split
splitvec <- sample(1:3, 100, replace = TRUE)
data(raschdat1, splitcr = splitvec)
res <- RM(raschdat1)
lrres <- LRtest(res)
lrres
summary(lrres)
plotGOF(lrres)