miceadds (version 2.2-0)

mi.anova: Analysis of Variance for Multiply Imputed Data Sets (Using the $D_2$ Statistic)

Description

This function combines $F$ values from analysis of variance using the $D_2$ statistic which is based on combining $\chi^2$ statistics (see Allison, 2001; micombine.F, micombine.chisquare).

Usage

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: A list with the following entries:

References

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

See Also

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.

Examples

#############################################################################
# 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 <- mice.1chain( nhanes2 , burnin=4 , iter=20 , Nimp=8 )
# 2-way analysis of variance (type 2)
an2a <- mi.anova(mi.res=mi.res, formula="bmi ~ age * chl" )
# 2-way analysis of variance (type 3)
an2b <- 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)