Performs model comparisons for a series of nested statistical models fitted using
## S3 method for class 'mitml.result' anova(object, ..., method = c("D3", "D4", "D2"), ariv = c("default", "positive", "robust"), data = NULL)
An object of class
Additional objects of class
A character string denoting the method used for the model comparison. Can be
A character string denoting how the ARIV is calculated. Can be
(optional) A list of imputed data sets (see 'Details').
This function performs likelihood-based comparisons between multiple statistical models fitted with
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 (Chan & Meng, 2019; Li, Meng, Raghunathan, & Rubin, 1991).
This function is essentially a wrapper for
testModels with the advantage that several models can be compared simultaneously.
For a list of supported models and further options for more specific model comparisons, see
ariv argument affects how the average relative increase in variance is calculated (see also
Note that the
D_4 method can fail if the data to which the model was fitted cannot be found.
In such a case, the
data argument can be used to specify the list of imputed data sets directly (see also
A list containing the results of each model comparison.
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.
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) # 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)
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