Artikel-Schlagworte: „Anova“

At first, if you are not familiar with R, you need to install R from CRAN and (under Win32) Tinn-R. Tinn-R enables to control R via script. If you use Linux, just install R via your distribution of choice and then there is an add-on for Emacs to control R (ESS). The next is to start R via Tinn-R or Emacs. Don’t forget to choose you ‚hotkeys‚ in Tinn-R (or any other editor of your choice to control R).

For multiple imputation, I choose the R-package ‚mice‚ from van Buuren et al. You have to install it manually. There are also other packages that deal with it, see:"imputation")

If you are not familiar with the R-style to formulate linear models, start with


or read the usual intros and manuals that are linked via CRAN (or the contributed documentations) – otherwise search on the internet with your search machine of choice with the add-on ‚cran‘ like ‚mulitple imputation cran‘ or ‚missing data cran‘, etc.

The folllowing are excerpts from the man-pages of ‚mice‘-package commandos.

?read.table       # import of data - see tutorials and intros to R
                  # of 'how to import data'
library(help=mice)# what is inside package 'mice'?
library(mice)     # load library for MI
data(nhanes)      # use data from 'mice'-package
nhanes            # show data
?mice             # produce Multivariate Imputation by Chained Equations
imp <- mice(nhanes)
?lm.mids          # Performs repeated linear regression
                  # on multiply imputed data set
lm.mids           # R-source code of 'lm.mids'
fit <- lm.mids(bmi~hyp+chl,data=imp)
?pool             #
pool(fit)         # pool results
summary(pool(fit))# better output
pool              # R-source code of 'pool'

That’s all. Multiple imputation (ordinary ANOVA) is quite easy to peform in R.