Fraction of missing information in multiple imputation
References
Load packages
library(mice)
Analysis
## Set seed
set.seed(20140619)
## Raw data
data(nhanes)
nhanes
## age bmi hyp chl
## 1 1 NA NA NA
## 2 2 22.7 1 187
## 3 1 NA 1 187
## 4 3 NA NA NA
## 5 1 20.4 1 113
## 6 3 NA NA 184
## 7 1 22.5 1 118
## 8 1 30.1 1 187
## 9 2 22.0 1 238
## 10 2 NA NA NA
## 11 1 NA NA NA
## 12 2 NA NA NA
## 13 3 21.7 1 206
## 14 2 28.7 2 204
## 15 1 29.6 1 NA
## 16 1 NA NA NA
## 17 3 27.2 2 284
## 18 2 26.3 2 199
## 19 1 35.3 1 218
## 20 3 25.5 2 NA
## 21 1 NA NA NA
## 22 1 33.2 1 229
## 23 1 27.5 1 131
## 24 3 24.9 1 NA
## 25 2 27.4 1 186
## Impute
imp <- mice(nhanes)
##
## iter imp variable
## 1 1 bmi hyp chl
## 1 2 bmi hyp chl
## 1 3 bmi hyp chl
## 1 4 bmi hyp chl
## 1 5 bmi hyp chl
## 2 1 bmi hyp chl
## 2 2 bmi hyp chl
## 2 3 bmi hyp chl
## 2 4 bmi hyp chl
## 2 5 bmi hyp chl
## 3 1 bmi hyp chl
## 3 2 bmi hyp chl
## 3 3 bmi hyp chl
## 3 4 bmi hyp chl
## 3 5 bmi hyp chl
## 4 1 bmi hyp chl
## 4 2 bmi hyp chl
## 4 3 bmi hyp chl
## 4 4 bmi hyp chl
## 4 5 bmi hyp chl
## 5 1 bmi hyp chl
## 5 2 bmi hyp chl
## 5 3 bmi hyp chl
## 5 4 bmi hyp chl
## 5 5 bmi hyp chl
## Fit models for each imputed dataset
fit <- with(data = imp, exp = lm(bmi ~ hyp + chl))
## Pool results
poolFit <- pool(fit)
## Print: The FMI for each coefficient is shown.
poolFit
## Call: pool(object = fit)
##
## Pooled coefficients:
## (Intercept) hyp chl
## 21.97735 -0.60095 0.02799
##
## Fraction of information about the coefficients missing due to nonresponse:
## (Intercept) hyp chl
## 0.2373 0.2159 0.2855
## Summary
summary(poolFit)
## est se t df Pr(>|t|) lo 95 hi 95 nmis fmi lambda
## (Intercept) 21.97735 4.50724 4.876 15.81 0.000174 12.41296 31.54175 NA 0.2373 0.1466
## hyp -0.60095 1.97686 -0.304 16.52 0.764929 -4.78108 3.57918 8 0.2159 0.1264
## chl 0.02799 0.02245 1.247 14.23 0.232568 -0.02008 0.07607 10 0.2855 0.1916