Tutorial

Installation

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

# install.packages('devtools')
devtools::install_github("MGallow/statr")

If there are any issues/bugs, please let me know: github. You can also contact me via my website.

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

Functions

`diagnostic()`

Similar to scatter, diagnostic creates diagnostic plots (residual and QQ) in ggplot:

# create diagnostic plots
cars %>% diagnostic(speed, dist)

`dsearch()`

dsearch is a dichotomous search algorithm that will minimize univariate strictly pseudoconvex functions. Let us consider the quadratic function centered at 0:

# quadratic
x = seq(-5, 5, 0.1)
y = x^2
data = data.frame(x, y)
ggplot(data) + geom_line(mapping = aes(x, y, color = "red"), show.legend = FALSE) + 
    ggtitle("y = x^2")

The minimum of this function clearly occurs at \(x = 0\) but we will confirm that with the dsearch function.

# first we define the quadratic function
g = function(x) {
    x^2
}

# dsearch
dsearch(g, -10, 10)

## new interval is -4.761905e-08 4.761905e-08

## [1] 0

We can see that dsearch confirms the minimum occurs at the suggested point.

`bsearch()`

bsearch is a bisection search algorithm that works very similarly to dsearch. The difference being that bsearch will find the root of the function – that is, when the function is equal to zero. Therefore, if we want to again minimize the quadratic function, we need to input the derivative of the quadratic function to find its minimum (recall that local minima and maxima occur when the derivative/gradient is equal to zero).

# derivative of quadratic
x = seq(-5, 5, 0.1)
dy = 2 * x
data = data.frame(x, dy)
ggplot(data) + geom_line(mapping = aes(x, dy, color = "red"), show.legend = FALSE) + 
    ggtitle("dy = 2x")

# define the derivative of quadratic
dg = function(x) {
    2 * x
}

# bsearch
bsearch(dg, -10, 10)

## new interval is 0 0

## [1] 0

We can see that bsearch confirms the minimum occurs at \(x = 0\).

`scatter()`

This function simply streamlines the process of creating a scatterplot with ggplot:

# create scatterplot
cars %>% scatter(speed, dist)

Matt Galloway