View source: R/anova.mitml.result.R

anova.mitml.result | R Documentation |

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

.

```
## S3 method for class 'mitml.result'
anova(object, ..., method = c("D3", "D4", "D2"),
ariv = c("default", "positive", "robust"),
data = NULL)
```

`object` |
An object of class |

`...` |
Additional objects of class |

`method` |
A character string denoting the method used for the model comparison. Can be |

`ariv` |
A character string denoting how the ARIV is calculated. Can be |

`data` |
(optional) A list of imputed data sets (see 'Details'). |

This function performs likelihood-based comparisons between multiple statistical models fitted with `with.mitml.list`

.
If possible, the models are compared using the `D_3`

statistic (Meng & Rubin, 1992).
If this method is unavailable, the `D_4`

or `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 `testModels`

.

The `ariv`

argument affects how the average relative increase in variance is calculated (see also `testModels`

).
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 `testModels`

).

A list containing the results of each model comparison.
A `print`

method is used for more readable output.

Simon Grund

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.

`with.mitml.list`

, `testModels`

```
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)
```

mitml documentation built on March 31, 2023, 7:01 p.m.

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