This vignette showcases the functions `regressionImp()` and `rangerImpute()`, which can both be used to generate imputations for several variables in a dataset using a formua interface.

### Data

For data, a subset of `sleep` is used. The columns have been selected deliberately to include some interactions between the missing values.

    library(VIM)
    library(magrittr)
    dataset <- sleep[, c("Dream", "NonD", "BodyWgt", "Span")]
    dataset$BodyWgt <- log(dataset$BodyWgt)
    dataset$Span <- log(dataset$Span)
    aggr(dataset)

    str(dataset)
    #> 'data.frame':    62 obs. of  4 variables:
    #>  $ Dream  : num  NA 2 NA NA 1.8 0.7 3.9 1 3.6 1.4 ...
    #>  $ NonD   : num  NA 6.3 NA NA 2.1 9.1 15.8 5.2 10.9 8.3 ...
    #>  $ BodyWgt: num  8.803 0 1.2194 -0.0834 7.8427 ...
    #>  $ Span   : num  3.65 1.5 2.64 NA 4.23 ...

### Imputation

In order to invoke the imputation methods, a formula is used to specify which variables are to be estimated and which variables should be used as regressors. We will start by imputing `NonD` based in `BodyWgt` and `Span`.

    imp_regression <- regressionImp(NonD ~ BodyWgt + Span, dataset)
    #> There still missing values in variable NonD . Probably due to missing values in the regressors.
    imp_ranger <- rangerImpute(NonD ~ BodyWgt + Span, dataset)
    aggr(imp_regression, delimiter = "_imp")

We can see that there are still missings in `NonD` for all observations where `Span` is unobserved. This is because the regression model could not be applied to those observations. The same is true for the values imputed via `rangerImpute()`.

### Diagnosing the results

As we can see in the next two plots, the correlation structure of `NonD` and `BodyWgt` is preserved by both imputation methods. In the case of `regressionImp()` all imputed values almost follow a straight line. This suggests that the variable `Span` had little to no effect on the model.

    imp_regression[, c("NonD", "BodyWgt", "NonD_imp")] %>%
      marginplot(delimiter = "_imp")

For `rangerImpute()` on the other hand, `Span` played an important role in the generation of the imputed values.

    imp_ranger[, c("NonD", "BodyWgt", "NonD_imp")] %>%
      marginplot(delimiter = "_imp")

    imp_ranger[, c("NonD", "Span", "NonD_imp")] %>%
      marginplot(delimiter = "_imp")

### Imputing multiple variables

To impute several variables at once, the formula in `rangerImpute()` and `regressionImp()` can be specified with more than one column name in the left hand side.

    imp_regression <- regressionImp(Dream + NonD ~ BodyWgt + Span, dataset)
    #> There still missing values in variable Dream . Probably due to missing values in the regressors.
    #> There still missing values in variable NonD . Probably due to missing values in the regressors.
    imp_ranger <- rangerImpute(Dream + NonD ~ BodyWgt + Span, dataset)
    aggr(imp_regression, delimiter = "_imp")

Again, there are missings left for both `Dream` and `NonD`.