README.Rmd

---
title: "statr"
output: github_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

[![Build Status](https://travis-ci.org/MGallow/statr.svg?branch=master)](https://travis-ci.org/MGallow/statr)
[![CRAN_Status_Badge](http://www.r-pkg.org/badges/version/statr)](https://cran.r-project.org/package=statr)

## Overview

`statr` is a personal R package that I have created for organizational/convenience purposes. This project is purely experimental! A (possibly incomplete) list of functions contained in the package can be found below:

* `tidy()` tidy's R package code and updates documentation
* `CVsplit()` splits data objects into training and testing sets
* `data_gen()` generates data for linear regression settings
* `dense()` generates dense matrices
* `denseQR()` generates dense matrices via spectral decomposition
* `tridiag()` generates tri-diagonal matrices
* `derivative()` approximates the derivative for a given function
* `diagnostic()` creates diagnostic plots using ggplot (residual and QQ)
* `dsearch()` is a dichotomous search algorithm for minimizing a univariate function
* `bsearch()` is a bi-section search algorithm for minimizing a univariate function
* `LASSO()` calculates lasso regression coefficient with optimal tuning
* `RIDGE()` calculates ridge regression coefficient with optimal tuning
* `scatter()` creates a scatterplot using ggplot

See [vignette](https://mgallow.github.io/statr/) or [manual](https://github.com/MGallow/statr/blob/master/statr.pdf).

## Installation

The easiest way to install is from the development version from Github:

```{r, eval = FALSE}
# install.packages("devtools")
devtools::install_github("MGallow/statr")
```

If there are any issues/bugs, please let me know: [github](https://github.com/MGallow/statr/issues). You can also contact me via my [website](https://mgallow.github.io/). Contributions are welcome!


## Usage

```{r, message = FALSE}
library(statr)
library(magrittr)

# we will use the iris data set
X = dplyr::select(iris, -c(Species, Sepal.Length)) %>% as.matrix
y = dplyr::select(iris, Sepal.Length) %>% as.matrix
y_class = ifelse(dplyr::select(iris, Species) == "setosa", 1, 0)

# let us split the data for testing and training
CV = CVsplit(X, y)

# we can do some exploratory analysis
# plot Sepal.Length v Sepal.Width
iris %>% scatter(Sepal.Length, Sepal.Width)

# plot diagnostic plots
iris %>% diagnostic(Sepal.Length, Sepal.Width)

# use the training data to fit ridge regression
RIDGE(CV$X.train, CV$Y.train)

# or lasso regression
statr::LASSO(CV$X.train, CV$Y.train)

# we can also generate our own data
data = data_gen(p = 10, r = 5, n = 100)
CV = CVsplit(data$X, data$Y)

# and again fit a ridge regression
statr::RIDGE(CV$X.train, CV$Y.train)

# we can also generate random matrices with may be useful
# for other applications
# tridiagonal matrices
tridiag(p = 5)$Omega %>% round(5)

# dense matrices
dense(p = 5)$Omega %>% round(5)

# compound symmetric matrices
compound(p = 5)$Omega %>% round(5)

```