mitml (version 0.3-1)

anova.mitml.result: Compare several nested models

Description

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

Usage

## S3 method for class 'mitml.result':
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 fit 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)

# multiple comparisons
fit2 <- with(implist, lmer(ReadAchiev ~ ReadDis + (1+ReadDis|ID), REML=FALSE))
anova(fit2,fit1,fit0)