---
title: "Using the SplitWise Package with the mtcars Dataset"
authors: "Marcell T. Kurbucz, Nikolaos Tzivanakis, Nilufer Sari Aslam, Adam M. Sykulski"
date: "`r Sys.Date()`"
output:
  html_document:
    theme: flatly
    highlight: tango
    toc: true
    toc_depth: 2
    toc_float: true
vignette: >
  %\VignetteIndexEntry{Using the SplitWise Package with the mtcars Dataset}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

```{r load-package, include = FALSE}
library(SplitWise)
```

# Introduction

The `SplitWise` package provides tools for transforming numeric variables in regression models by either applying a single-split dummy encoding or retaining them as linear terms. This vignette demonstrates the application of `SplitWise` using the `mtcars` dataset, showcasing both univariate and iterative transformation approaches.

# The mtcars Dataset

The `mtcars` dataset is a built-in R dataset that comprises fuel consumption and 10 aspects of automobile design and performance for 32 automobiles (1973–74 models).

```{r mtcars}
# Load the mtcars dataset
data(mtcars)
```

# Iterative Transformations

The iterative transformation approach evaluates each variable's transformation in the context of variables already added to the model. Here is an example using forward stepwise selection:

```{r iterative-transformation}
# Apply iterative transformations with forward stepwise selection
model_iter <- splitwise(
  mpg ~ .,
  data = mtcars,
  transformation_mode = "iterative",
  direction = "backward",
  verbose = FALSE
)

# Display the summary of the model
summary(model_iter)

# Print the model details
print(model_iter)
```

# Univariate Transformations

In the univariate transformation approach, each numeric predictor is transformed independently without considering the context of other variables. Below is an example of applying univariate transformations with backward stepwise selection:

```{r univariate-transformation}
# Apply univariate transformations with backward stepwise selection
model_uni <- splitwise(
  mpg ~ .,
  data = mtcars,
  transformation_mode = "univariate",
  direction = "backward",
  verbose = FALSE
)

# Display the summary of the model
summary(model_uni)

# Print the model details
print(model_uni)
```

# Conclusion

This vignette illustrated how to utilize the `SplitWise` package to perform both univariate and iterative transformations on the `mtcars` dataset. Depending on the analysis requirements, users can choose the appropriate transformation approach to enhance their regression models.
