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.

Installing

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     NA 1814 <NA>
#> 2   yes  56     Post    12     II      7      NA     77 2018 <NA>
#> 3   yes  58     <NA>    35     II     NA      52    271  712 <NA>
#> 4   yes  NA     Post    17   <NA>      4      60     NA   NA    1
#> 5  <NA>  NA     <NA>    NA     II     NA      26     65  772    1
#> 6    no  32      Pre    57    III     24       0     13  448 <NA>

You 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
#> 1    no 70.00000     Post 21.00000     II  3.000000 107.2613 188.43849 1814.000
#> 2   yes 56.00000     Post 12.00000     II  7.000000 115.5780  77.00000 2018.000
#> 3   yes 58.00000     Post 35.00000     II  5.829284  52.0000 271.00000  712.000
#> 4   yes 57.86864     Post 17.00000     II  4.000000  60.0000  77.58405 1051.515
#> 5    no 51.75196     Post 30.54075     II  5.685274  26.0000  65.00000  772.000
#> 6    no 32.00000      Pre 57.00000    III 24.000000   0.0000  13.00000  448.000
#>   cens
#> 1    0
#> 2    0
#> 3    1
#> 4    1
#> 5    1
#> 6    1

Citing

To cite missCforest in publications please use:

El Badisy I (2023). missCforest: Ensemble Conditional Trees for Missing Data Imputation. R package version 0.0.8, https://CRAN.R-project.org/package=missCforest.

A BibTeX entry for LaTeX users is

  @Manual{,
    title = {missCforest: Ensemble Conditional Trees for Missing Data Imputation},
    author = {Imad {El Badisy}},
    year = {2023},
    note = {R package version 0.0.8},
    url = {https://CRAN.R-project.org/package=missCforest},
  }

Contributing

If you encounter any bugs or have an idea for contribution, please submit an issue.
Please include a reprex for reproducibility.