blorr/README.Rmd at master · rsquaredacademy/blorr · GitHub

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---
output: github_document
---

<!-- README.md is generated from README.Rmd. Please edit that file -->

```{r, echo = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "tools/README-"
)
options(tibble.width = Inf)
```

# blorr

> Tools for building binary logistic regression models

<!-- badges: start -->
[![CRAN_Status_Badge](https://www.r-pkg.org/badges/version/blorr)](https://cran.r-project.org/package=blorr)
[![R-CMD-check](https://github.com/rsquaredacademy/blorr/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/rsquaredacademy/blorr/actions/workflows/R-CMD-check.yaml)
[![Coverage status](https://app.codecov.io/gh/rsquaredacademy/blorr/branch/master/graph/badge.svg)](https://app.codecov.io/github/rsquaredacademy/blorr?branch=master)

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## Overview

Tools designed to make it easier for users, particularly beginner/intermediate
R users to build logistic regression models. Includes comprehensive regression
output, variable selection procedures, model validation techniques and a
'shiny' app for interactive model building.

## Installation

```{r gh-installation, eval = FALSE}
# Install blorr from CRAN
install.packages("blorr")

# Install development version from GitHub
# install.packages("devtools")
devtools::install_github("rsquaredacademy/blorr")

# Install the development version from `rsquaredacademy` universe
install.packages("blorr", repos = "https://rsquaredacademy.r-universe.dev")
```

## Articles

- [A Short Introduction to the blorr Package](https://blorr.rsquaredacademy.com/articles/introduction.html)

## Usage

blorr uses consistent prefix `blr_*` for easy tab completion.

```{r libs}
library(blorr)
library(magrittr)
```

### Bivariate Analysis

```{r bivar}
blr_bivariate_analysis(hsb2, honcomp, female, prog, race, schtyp)
```

### Weight of Evidence & Information Value

```{r woeiv}
blr_woe_iv(hsb2, prog, honcomp)
```

### Model

```{r model}
# create model using glm
model <- glm(honcomp ~ female + read + science, data = hsb2,
             family = binomial(link = 'logit'))
```

### Regression Output

```{r regress}
blr_regress(model)
```

### Model Fit Statistics

```{r mfs}
blr_model_fit_stats(model)
```

### Confusion Matrix

```{r conf}
blr_confusion_matrix(model)
```

### Hosmer Lemeshow Test

```{r hoslem}
blr_test_hosmer_lemeshow(model)
```

### Gains Table

```{r gains}
blr_gains_table(model)
```

### Lift Chart

```{r lift}
model %>%
  blr_gains_table() %>%
  plot()
```

### ROC Curve

```{r roc}
model %>%
  blr_gains_table() %>%
  blr_roc_curve()
```

### KS Chart

```{r kschart}
model %>%
  blr_gains_table() %>%
  blr_ks_chart()
```

### Lorenz Curve

```{r}
blr_lorenz_curve(model)
```

## Getting Help

If you encounter a bug, please file a minimal reproducible example using
[reprex](https://reprex.tidyverse.org/index.html) on github. For questions and
clarifications, use [StackOverflow](https://stackoverflow.com/).

## Code of Conduct

Please note that the blorr project is released with a [Contributor Code of Conduct](https://blorr.rsquaredacademy.com/CODE_OF_CONDUCT.html). By contributing to this project, you agree to abide by its terms.