This vignette showcases the functions `regressionImp()`

and `rangerImpute()`

, which can both be used to generate imputations for several variables in a dataset using a formula interface.

For data, a subset of `sleep`

is used. The columns have been selected deliberately to include some interactions between the missing values.

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 for `regrssionImp()`

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()`

.

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.

For `rangerImpute()`

on the other hand, `Span`

played an important role in the generation of the imputed values.

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`

.

In order to validate the performance of `regressionImp()`

the `iris`

dataset is used. Firstly, some values are randomly set to `NA`

.

```
library(reactable)
data(iris)
df <- iris
colnames(df) <- c("S.Length","S.Width","P.Length","P.Width","Species")
# randomly produce some missing values in the data
set.seed(1)
nbr_missing <- 50
y <- data.frame(row=sample(nrow(iris),size = nbr_missing,replace = T),
col=sample(ncol(iris)-1,size = nbr_missing,replace = T))
y<-y[!duplicated(y),]
df[as.matrix(y)]<-NA
aggr(df)
```

We can see that there are missings in all variables and some observations reveal missing values on several points. In the next step we perform a multiple variable imputation and `Species`

serves as a regressor.

```
imp_regression <- regressionImp(S.Length + S.Width + P.Length + P.Width ~ Species, df)
aggr(imp_regression, delimiter = "imp")
```

The plot indicates that all missing values have been imputed by the `regressionImp()`

algorithm. The following table displays the rounded first five results of the imputation for all variables.