# mi.anova: Analysis of Variance for Multiply Imputed Data Sets (Using... In alexanderrobitzsch/miceadds: Some Additional Multiple Imputation Functions, Especially for 'mice'

## Description

This function combines F values from analysis of variance using the D_2 statistic which is based on combining χ^2 statistics (see Allison, 2001, Grund, Luedtke & Robitzsch, 2016; `micombine.F`, `micombine.chisquare`).

## Usage

 `1` ```mi.anova(mi.res, formula, type=2) ```

## Arguments

 `mi.res` Object of class `mids` or `mids.1chain` `formula` Formula for `lm` function. Note that this can be also a string. `type` Type for ANOVA calculations. For `type=3`, the `car::Anova` function from the car package is used.

## Value

A list with the following entries:

 `r.squared` Explained variance R^2 `anova.table` ANOVA table

## References

Allison, P. D. (2002). Missing data. Newbury Park, CA: Sage.

Grund, S., Luedtke, O., & Robitzsch, A. (2016). Pooling ANOVA results from multiply imputed datasets: A simulation study. Methodology, 12, 75-88.

This function uses `micombine.F` and `micombine.chisquare`.

See `mice::pool.compare` and `mitml::testModels` for model comparisons based on the D_1 statistic. The D_2 statistic is also included in `mitml::testConstraints`.

The D_1, D_2 and D_3 statistics are also included in the mice package in functions `mice::D1`, `mice::D2` and `mice::D3`.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27``` ```############################################################################# # EXAMPLE 1: nhanes2 data | two-way ANOVA ############################################################################# library(mice) library(car) data(nhanes2, package="mice") set.seed(9090) # nhanes data in one chain and 8 imputed datasets mi.res <- miceadds::mice.1chain( nhanes2, burnin=4, iter=20, Nimp=8 ) # 2-way analysis of variance (type 2) an2a <- miceadds::mi.anova(mi.res=mi.res, formula="bmi ~ age * chl" ) # 2-way analysis of variance (type 3) an2b <- miceadds::mi.anova(mi.res=mi.res, formula="bmi ~ age * chl", type=3) #****** analysis based on first imputed dataset # extract first dataset dat1 <- mice::complete( mi.res\$mids ) # type 2 ANOVA lm1 <- stats::lm( bmi ~ age * chl, data=dat1 ) summary( stats::aov( lm1 ) ) # type 3 ANOVA lm2 <- stats::lm( bmi ~ age * chl, data=dat1, contrasts=list(age=contr.sum)) car::Anova( lm2, type=3) ```

alexanderrobitzsch/miceadds documentation built on Nov. 17, 2018, 1:18 p.m.