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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r setup, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
[](https://travis-ci.org/kvantas/missTune)
[](https://ci.appveyor.com/project/kvantas/missTune)
[](https://codecov.io/github/kvantas/missTune?branch=master)
[](https://cran.r-project.org/package=missTune)
[](https://www.tidyverse.org/lifecycle/#experimental)
# missTune
This package is an alternative implementation of the `missForest` and `missRanger` packages using tuned Random Forests. The `tuneRF` function from the `randomForest` is used internally to find the optimal `mtry` parameter.
## Installation
You can install the development version from Github with:
``` r
# install.packages("devtools")
devtools::install_github("kvantas/missTune")
```
## Example
This is a basic example about infilling a dataset.
```{r "infill_iris"}
library(missTune)
# create random na values
iris_na <- generate_na(iris, p = 0.1, seed = 123)
# infill values
res_imp <- miss_tune(x_miss = iris_na, num_trees = 100, verbose = TRUE)
```
Let's view the original data-set with the missing values and the infilled one.
```{r "view data"}
head(iris_na)
head(res_imp$x_imp)
```
And finally let's create a plot with the mean out of bag error during the iterations of the algorithm.
```{r "plot_oob_error"}
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](CODE_OF_CONDUCT.md). By contributing to this project, you agree to abide by its terms.