# imputeGeneric

The goal of imputeGeneric is to ease the implementation of imputation functions.

## Installation

You can install the development version of imputeGeneric from GitHub with:

```
# install.packages("devtools")
devtools::install_github("torockel/imputeGeneric")
```

## Purpose

The aim of imputeGeneric is to make the implementation and usage of imputation methods easier. The main function of the package is `impute_iterative()`

. This function can turn any parsnip model into an imputation method. Furthermore, other customized approaches can be used in a general imputation framework. For more information, see the documentations of `impute_iterative()`

, `impute_supervised()`

, `impute_unsupervised()`

and the following examples.

## Examples

### Simple example

The use of a parsnip model for imputation is demonstrated using regression trees from the rpart package via parsnip (`decision_tree("regression")`

). First, a data set with missing values is created. Then, this data set is imputed once with regression trees using only completely observed rows and columns for the model building.

```
library(imputeGeneric)
library(parsnip)
# create data set
set.seed(123)
ds_mis <- data.frame(X = rnorm(100), Y = rnorm(100))
ds_mis$Z <- 5 + 2* ds_mis$X + ds_mis$Y + rnorm(100)
ds_mis$Z[sample.int(100, 30)] <- NA
ds_mis$Y[sample.int(100, 20)] <- NA
# impute data set
ds_imp <- impute_iterative(ds_mis, decision_tree("regression"), max_iter = 1)
anyNA(ds_imp)
#> [1] FALSE
```

To use other parsnip models instead of regression trees, only the `model_spec_parsnip`

argument must be altered. E.g. for linear regression instead of regression trees use `linear_reg()`

.

```
ds_imp_lm <- impute_iterative(ds_mis, linear_reg(), max_iter = 1)
anyNA(ds_imp_lm)
#> [1] FALSE
```

### More complex example

Many aspects of the imputation can be specified and customized. The missing values can be initially imputed e.g. with per column mean values (`initial_imputation_fun = missMethods::impute_mean`

). In addition, all objects and columns can be used for the imputation models (`rows_used_for_imputation = "all"`

and `cols_used_for_imputation = "all"`

). Furthermore, the imputation can be iterative. The iteration will be stopped, if either the difference between two imputed data sets falls below a threshold (`stop_fun = stop_ds_difference, stop_fun_args = list(eps = 0.1)`

) or the maximum number of iterations (`max_iter = 5`

) is reached.

```
ds_imp2 <- impute_iterative(
ds_mis, decision_tree("regression"),
initial_imputation_fun = missMethods::impute_mean,
cols_used_for_imputation = "all",
rows_used_for_imputation = "all",
stop_fun = stop_ds_difference,
stop_fun_args = list(eps = 0.1),
max_iter = 5)
anyNA(ds_imp2)
#> [1] FALSE
```