eRm (version 0.9-5)

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", 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

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 a

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.

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.

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.

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)