LRtest {eRm}R Documentation

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", tlab = "item", ylim = c(-3, 3), xlim = c(-3, 3), type = "p", pos = "4", ...)
## 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 a vector which assigns each person to a certain subgroup (e.g., following an external criterion).
se If TRUE standard errors for beta's are computed.
x 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.
tlab Specification of item labels: "item" prints the item names, "number" gives integers corresponding to order of the beta parameters, if "none" no labels are printed. "identify" allows for an interactive labelling. Initially no labels are printed, after clicking close to an item point the corresponding label is added. The identification process is terminated by clicking the second button and selecting 'Stop' from the menu, or from the 'Stop' menu on the graphics window. For more information see identify.
xlim Limits on x-axis.
ylim Limits on y-axis.
type Plotting type.(see plot)
pos Position of the item label (see text)
... Additional graphical parameters.

Details

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.

Value

LRtest returns an object of class LR containing:

LR LR-value.
df Degrees of freedom of the test statistic.
Chisq Chi-square value with corresponding df.
pvalue P-value of the test.
likgroup Log-likelihood values for the subgroups
betalist List of beta parameters for the subgroups.
selist List of standard errors of beta's.
etalist List of eta parameters for the subgroups.

Author(s)

Patrick Mair, Reinhold Hatzinger

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.

See Also

Waldtest

Examples


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

[Package eRm version 0.9-5 Index]