R: Compare several nested models

anova.mitml.result {mitml} | R Documentation

## Compare several nested models

### Description

Performs model comparisons for a series of nested statistical models fitted 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.

### 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`.

### Value

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

### Author(s)

Simon Grund

### 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)

[Package mitml version 0.3-5 Index]