mitml (version 0.3-4)

anova.mitml.result: Compare several nested models

Description

Performs model comparisons for a series of nested statistical models fitted using with.mitml.list.

Usage

"anova"(object, ...)

Arguments

object

An object of class mitml.result as produced by with.mitml.list.

...

Additional objects of class mitml.result to be included in the comparison.

Value

Returns a list containing the results of each model comparison. A print method is used for better readable console output.

Details

This function performs several model comparisons between models fitted using with.mitml.list. If possible, the models are compared using the $D_3$ statistic (Meng & Rubin, 1992). If this method is unavailable, the $D_2$ statistic is used instead (Li, Meng, Raghunathan, & Rubin, 1991). The $D_3$ method currently supports linear models and linear mixed-effects models with a single cluster variable as estimated by lme4 or nlme (see Laird, Lange, & Stram, 1987).

This function is essentially a wrapper for testModels with the advantage that several models can be compared simultaneously. All model comparisons are likelihood-based. For further options for model comparisons (e.g., Wald-based procedures) and finer control, see testModels.

References

Meng, X.-L., & Rubin, D. B. (1992). Performing likelihood ratio tests with multiply-imputed data sets. Biometrika, 79, 103-111.

Laird, N., Lange, N., & Stram, D. (1987). Maximum likelihood computations with repeated measures: Application of the em algorithm. Journal of the American Statistical Association, 82, 97-105.

Li, K. H., Raghunathan, T. E., & Rubin, D. B. (1991). Large-sample significance levels from multiply imputed data using moment-based statistics and an F reference distribution. Journal of the American Statistical Association, 86, 1065-1073.

See Also

with.mitml.list, testModels

Examples

require(lme4)
data(studentratings)

fml <- ReadDis + SES ~ ReadAchiev + (1|ID)
imp <- panImpute(studentratings, formula=fml, n.burn=1000, n.iter=100, m=5)

implist <- mitmlComplete(imp, print=1:5)

# simple comparison (same as testModels)
fit0 <- with(implist, lmer(ReadAchiev ~ (1|ID), REML=FALSE))
fit1 <- with(implist, lmer(ReadAchiev ~ ReadDis + (1|ID), REML=FALSE))
anova(fit1,fit0)

## Not run: 
# # multiple comparisons
# fit2 <- with(implist, lmer(ReadAchiev ~ ReadDis + (1+ReadDis|ID), REML=FALSE))
# anova(fit2,fit1,fit0)
# ## End(Not run)