tmt_lrtest: Computation of Andersen's Likelihood-Ratio Test

In tmt: Estimation of the Rasch Model for Multistage Tests

Computation of Andersen's Likelihood-Ratio Test

Description

This function applies the Likelihood Ratio Test of Andersen. Note that all persons with raw score equal to "median" are assigned to the lower group in cases of a median split. Is is also allowed to split after "mean" or submit any dichotomous vector as split criteria.

Usage

tmt_lrtest(object, split = "median", cores = NULL, se = TRUE, ...)

Arguments

object	it is necessary to submit an object of the function mst or nmst
split	default is the split criteria "median" of the raw score, optional are "mean" or any dichotomous vector
cores	submit integer of cores you would like to apply
se	logical: if true, the standard error is estimated
...	further arguments for the tmt_rm function

Value

List with following entries

data_orig	Submitted data frame with item responses
betapars_subgroup	List of item parameters (difficulty) for each subgroup
se.beta_subgroup	List of standard errors of the estimated item parameters
model	Used model ((mst) for Rasch model with multistage design)
LRvalue	LR-value
df	Degrees of freedoms for the test statistic
pvalue	P-value of the likelihood ratio test
loglik_subgroup	Log-likelihoods for the subgroups
split_subgroup	List of split vector for each subgroup
call	Submitted arguments for the function (matched call)
fitobj	List of objects from subgroup estimation

Author(s)

Jan Steinfeld

References

• Andersen, E. B. (1973). A goodness of fit test for the Rasch model. Psychometrika, 38(1), 123-140.

• Fischer, G. H., & Molenaar, I. W. (Eds.). (2012). Rasch models: Foundations, recent developments, and applications. Springer Science & Business Media.

See Also

tmt_rm

Examples

# example for tmt_lrtest
#############################################################################
# Example Rasch model and Likelihood Ratio Test
#############################################################################
dat <- tmt:::sim.rm(theta = 100, b = 10, seed = 1111)
dat.rm <- tmt_rm(dat = dat)
dat.lrt <- tmt_lrtest(dat.rm)
summary(dat.lrt)

tmt documentation built on May 17, 2022, 5:09 p.m.