---
title: "Model based Imputation Methods"
author: Gregor de Cillia
output:
  rmarkdown::html_vignette:
    highlight: default
vignette: >
  %\VignetteIndexEntry{Model based Imputation Methods}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 6,
  fig.align = "center"
)
```

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.

## Data

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

```{r setup, message = FALSE}
library(VIM)
dataset <- sleep[, c("Dream", "NonD", "BodyWgt", "Span")]
dataset$BodyWgt <- log(dataset$BodyWgt)
dataset$Span <- log(dataset$Span)
aggr(dataset)
str(dataset)
```

## 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`. 

```{r}
imp_regression <- regressionImp(NonD ~ BodyWgt + Span, dataset)
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()`. 

## 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.

```{r, fig.height=5}
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.

```{r, fig.height=5}
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.

```{r}
imp_regression <- regressionImp(Dream + NonD ~ BodyWgt + Span, dataset)
imp_ranger <- rangerImpute(Dream + NonD ~ BodyWgt + Span, dataset)
aggr(imp_regression, delimiter = "_imp")
```

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



## Performance of method

In order to validate the performance of `regressionImp()` the `iris` dataset is used. Firstly, some values are randomly set to `NA`. 

```{r}
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)
sapply(df, function(x)sum(is.na(x)))
```

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.

```{r}
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.  



```{r echo=F,warning=F}
results <- cbind("TRUE1" = as.numeric(iris[as.matrix(y[which(y$col==1),])]),
                 "IMPUTED1" = round(as.numeric(imp_regression[as.matrix(y[which(y$col==1),])]),2),
                 "TRUE2" = as.numeric(iris[as.matrix(y[which(y$col==2),])]),
                 "IMPUTED2" = round(as.numeric(imp_regression[as.matrix(y[which(y$col==2),])]),2),
                 "TRUE3" = as.numeric(iris[as.matrix(y[which(y$col==3),])]),
                 "IMPUTED3" = round(as.numeric(imp_regression[as.matrix(y[which(y$col==3),])]),2),
                 "TRUE4" = as.numeric(iris[as.matrix(y[which(y$col==4),])]),
                 "IMPUTED4" = round(as.numeric(imp_regression[as.matrix(y[which(y$col==4),])]),2))[1:5,]

reactable(results, columns = list(
    TRUE1 = colDef(name = "True"),
    IMPUTED1 = colDef(name = "Imputed"),
    TRUE2 = colDef(name = "True"),
    IMPUTED2 = colDef(name = "Imputed"),
    TRUE3 = colDef(name = "True"),
    IMPUTED3 = colDef(name = "Imputed"),
    TRUE4 = colDef(name = "True"),
    IMPUTED4 = colDef(name = "Imputed")
  ),
    columnGroups = list(
    colGroup(name = "S.Length", columns = c("TRUE1", "IMPUTED1")),
    colGroup(name = "S.Width", columns = c("TRUE2", "IMPUTED2")),
    colGroup(name = "P.Length", columns = c("TRUE3", "IMPUTED3")),
    colGroup(name = "P.Width", columns = c("TRUE4", "IMPUTED4"))
  ),
  striped = TRUE,
  highlight = TRUE,
  bordered = TRUE
)

```




