missTune

This package is an alternative implementation of the missForest and missRanger packages using tuned Random Forests. The tuneRF function from the randomForest is used internally to find the optimal mtry parameter.

Installation

You can install the development version from Github with:

# install.packages("devtools")
devtools::install_github("kvantas/missTune")

Example

This is a basic example about infilling a dataset.

library(missTune)
# create random na values
iris_na <- generate_na(iris, p = 0.1, seed = 123)

# infill values
res_imp <- miss_tune(x_miss = iris_na, num_trees = 100, verbose = TRUE)
#> iteration 1 in progress...
#> Sepal.Length  Sepal.Width Petal.Length  Petal.Width      Species 
#>    0.3460387    0.5778749    0.1487706    0.1839707    0.1333333
#> iteration 2 in progress...
#> Sepal.Length  Sepal.Width Petal.Length  Petal.Width      Species 
#>   0.25616110   0.44955516   0.05855077   0.09440227   0.11333333
#> iteration 3 in progress...
#> Sepal.Length  Sepal.Width Petal.Length  Petal.Width      Species 
#>   0.23369627   0.46068315   0.04231241   0.07152515   0.12666667
#> iteration 4 in progress...
#> Sepal.Length  Sepal.Width Petal.Length  Petal.Width      Species 
#>   0.21898068   0.45219127   0.04015487   0.07033734   0.14000000
#> iteration 5 in progress...
#> Sepal.Length  Sepal.Width Petal.Length  Petal.Width      Species 
#>   0.21780414   0.43343415   0.03696376   0.06442391   0.13333333
#> iteration 6 in progress...
#> Sepal.Length  Sepal.Width Petal.Length  Petal.Width      Species 
#>   0.20834627   0.43931631   0.03779168   0.06757198   0.14666667

Let’s view the original data-set with the missing values and the infilled one.

head(iris_na)
#>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> 1          5.1         3.5          1.4         0.2  setosa
#> 2          4.9         3.0           NA         0.2  setosa
#> 3          4.7         3.2          1.3          NA    <NA>
#> 4          4.6          NA          1.5         0.2  setosa
#> 5           NA         3.6          1.4         0.2  setosa
#> 6          5.4         3.9          1.7         0.4  setosa
head(res_imp$x_imp)
#>   Sepal.Length Sepal.Width Petal.Length Petal.Width    Species
#> 1     5.100000    3.500000     1.400000   0.2000000     setosa
#> 2     4.900000    3.000000     1.469838   0.2000000     setosa
#> 3     4.700000    3.200000     1.300000   0.5044111 versicolor
#> 4     4.600000    3.305738     1.500000   0.2000000     setosa
#> 5     5.115963    3.600000     1.400000   0.2000000     setosa
#> 6     5.400000    3.900000     1.700000   0.4000000     setosa

And finally let’s create a plot with the mean out of bag error during the iterations of the algorithm.

library(ggplot2)
mean_errors <- unlist(lapply(res_imp$oob_list, mean))
ggplot()+
  geom_line(aes(x = 1: length(mean_errors), mean_errors)) +
  scale_x_continuous(breaks = 1: length(mean_errors)) + 
  xlab("Iteration") + ylab("Error")+
  theme_bw()

Meta

Please note that the missTune project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.