missCforest
missCforest is an Ensemble Conditional Trees algorithm for Missing
Data Imputation. It performs single imputation based on the Cforest
algorithm
which is an ensemble of Conditional Inference
Trees.
The aim of missCforest is to produce a complete dataset using an
iterative prediction approach by predicting missing values after
learning from the complete cases.
Installation
You can install the development version of missCforest as follow:
#install.packages("devtools")
devtools::install_github("ielbadisy/missCforest")Examples
library(missCforest)
#> Loading required package: partykit
#> Loading required package: grid
#> Loading required package: libcoin
#> Loading required package: mvtnorm
# import the GBSG2 dataset
library(TH.data)
#> Loading required package: survival
#> Loading required package: MASS
#>
#> Attaching package: 'TH.data'
#> The following object is masked from 'package:MASS':
#>
#> geyser
data("GBSG2")
# consider the cens variable as a factor
GBSG2$cens <- as.factor(GBSG2$cens)
# introduce randomly 30% of NA to variables
datNA <- missForest::prodNA(GBSG2, 0.2)
head(datNA)
#> horTh age menostat tsize tgrade pnodes progrec estrec time cens
#> 1 no 70 Post 21 II 3 NA 66 1814 1
#> 2 <NA> 56 Post 12 II 7 61 77 2018 1
#> 3 yes 58 Post 35 II 9 52 271 712 1
#> 4 yes NA <NA> NA II 4 NA 29 NA 1
#> 5 no NA Post NA II 1 26 65 772 1
#> 6 <NA> 32 Pre 57 III 24 NA 13 NA 1You can impute all the missing values using all the possible combinations of the imputation model formula:
impdat <- missCforest(datNA, .~.,
ntree = 300L,
minsplit = 20L,
minbucket = 7L,
alpha = 0.05,
cores = 4)
head(impdat)
#> horTh age menostat tsize tgrade pnodes progrec estrec time cens
#> 1 no 70.00000 Post 21.00000 II 3 88.68623 66 1814.0000 1
#> 2 yes 56.00000 Post 12.00000 II 7 61.00000 77 2018.0000 1
#> 3 yes 58.00000 Post 35.00000 II 9 52.00000 271 712.0000 1
#> 4 yes 53.31934 Post 27.15198 II 4 64.72325 29 970.5889 1
#> 5 no 56.31297 Post 25.92921 II 1 26.00000 65 772.0000 1
#> 6 no 32.00000 Pre 57.00000 III 24 48.91202 13 521.7364 1
