mi.anova: Analysis of Variance for Multiply Imputed Data Sets (Using...

Description Usage Arguments Value Author(s) References See Also Examples

View source: R/mi.anova.R

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; micombine.F, micombine.chisquare).

Usage

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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

Author(s)

Alexander Robitzsch

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

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#############################################################################
# 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)

Example output

Loading required package: mice
* miceadds 2.5-9 (2017-06-17 14:42:44)
************ BURNIN PHASE | Iterations 1 - 4 ******************

 iter imp variable
  1   1  bmi  hyp  chl
  2   1  bmi  hyp  chl
  3   1  bmi  hyp  chl
  4   1  bmi  hyp  chl


************ IMPUTATION PHASE | Iterations 5 - 20 ******************

 --- Imputation 1 | Iterations 4 - 6 

 iter imp variable
  1   1  bmi  hyp  chl
  2   1  bmi  hyp  chl

 --- Imputation 2 | Iterations 6 - 8 

 iter imp variable
  1   1  bmi  hyp  chl
  2   1  bmi  hyp  chl

 --- Imputation 3 | Iterations 8 - 10 

 iter imp variable
  1   1  bmi  hyp  chl
  2   1  bmi  hyp  chl

 --- Imputation 4 | Iterations 10 - 12 

 iter imp variable
  1   1  bmi  hyp  chl
  2   1  bmi  hyp  chl

 --- Imputation 5 | Iterations 12 - 14 

 iter imp variable
  1   1  bmi  hyp  chl
  2   1  bmi  hyp  chl

 --- Imputation 6 | Iterations 14 - 16 

 iter imp variable
  1   1  bmi  hyp  chl
  2   1  bmi  hyp  chl

 --- Imputation 7 | Iterations 16 - 18 

 iter imp variable
  1   1  bmi  hyp  chl
  2   1  bmi  hyp  chl

 --- Imputation 8 | Iterations 18 - 20 

 iter imp variable
  1   1  bmi  hyp  chl
  2   1  bmi  hyp  chl
Univariate ANOVA for Multiply Imputed Data (Type 2)  

lm Formula:  bmi ~ age * chl
R^2=0.5755 
..........................................................................
ANOVA Table 
                 SSQ df1      df2 F value  Pr(>F)    eta2 partial.eta2
age         105.2599   2 14.98955  1.5163 0.25136 0.22985      0.35127
chl         112.3693   1 23.72348  5.1846 0.03210 0.24538      0.36630
age:chl      45.9228   2 39.30677  1.1884 0.31543 0.10028      0.19109
Residual    194.3981  NA       NA      NA      NA      NA           NA
Univariate ANOVA for Multiply Imputed Data (Type 3)  

lm Formula:  bmi ~ age * chl
R^2=0.3364 
..........................................................................
ANOVA Table 
               SSQ df1       df2 F value  Pr(>F)    eta2 partial.eta2
age       24.99739   2 129.96671  0.8575 0.42661 0.08534      0.11394
chl       27.60494   1  21.01782  0.6750 0.42052 0.09424      0.12434
age:chl   45.92280   2  39.30677  1.1884 0.31543 0.15677      0.19109
Residual 194.39813  NA        NA      NA      NA      NA           NA
            Df Sum Sq Mean Sq F value Pr(>F)  
age          2  35.80   17.90   1.164 0.3335  
chl          1  79.76   79.76   5.188 0.0345 *
age:chl      2  55.19   27.60   1.795 0.1932  
Residuals   19 292.14   15.38                 
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Anova Table (Type III tests)

Response: bmi
            Sum Sq Df F value    Pr(>F)    
(Intercept) 326.20  1 21.2155 0.0001928 ***
age          31.63  2  1.0285 0.3765944    
chl          25.82  1  1.6790 0.2105836    
age:chl      55.19  2  1.7948 0.1932151    
Residuals   292.14 19                      
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Warning message:
system call failed: Cannot allocate memory 

miceadds documentation built on June 20, 2017, 9:10 a.m.

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