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 or manual.

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. Contributions are welcome!

Usage

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)

## $betas
##                     [,1]
## Sepal.Width  0.006182588
## Petal.Length 0.008078388
## Petal.Width  0.002630500
## 
## $lam
## [1] 2233.731

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

## $betas
##                   [,1]
## Sepal.Width  0.0000000
## Petal.Length 0.3305527
## Petal.Width  0.0000000
## 
## $lam
## [1] 17.29497

# 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)

## $betas
##              s0           s0           s0           s0           s0
## V1   0.22361780 -0.115761718  0.104229847 -0.072600428 -0.154270994
## V2   0.02305304 -0.016964647  0.006759119 -0.041432524 -0.046074158
## V3   0.15874949  0.099007474  0.212525724  0.006138727  0.017029184
## V4   0.09539731 -0.002189673  0.064124189  0.092066074  0.026877249
## V5  -0.04285759 -0.041315878  0.076181904  0.104015778 -0.026374616
## V6   0.00579039  0.031399701  0.164809901  0.149226048 -0.031209118
## V7   0.04179391  0.033475039 -0.017651981  0.044214675 -0.135049107
## V8   0.24074144 -0.105872848 -0.214755207 -0.048318457 -0.076911694
## V9  -0.06354331 -0.082274759 -0.411792495 -0.054637518  0.055296303
## V10  0.11876787 -0.077535702 -0.048367468  0.057315345 -0.009928922
## 
## $lam
## [1] 0.7241429

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

##          [,1]     [,2]     [,3]     [,4]     [,5]
## [1,]  1.96078 -1.37255  0.00000  0.00000  0.00000
## [2,] -1.37255  2.92157 -1.37255  0.00000  0.00000
## [3,]  0.00000 -1.37255  2.92157 -1.37255  0.00000
## [4,]  0.00000  0.00000 -1.37255  2.92157 -1.37255
## [5,]  0.00000  0.00000  0.00000 -1.37255  1.96078

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

##          [,1]     [,2]     [,3]     [,4]     [,5]
## [1,]  8.04348 -1.95652 -1.95652 -1.95652 -1.95652
## [2,] -1.95652  8.04348 -1.95652 -1.95652 -1.95652
## [3,] -1.95652 -1.95652  8.04348 -1.95652 -1.95652
## [4,] -1.95652 -1.95652 -1.95652  8.04348 -1.95652
## [5,] -1.95652 -1.95652 -1.95652 -1.95652  8.04348

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

##      [,1] [,2] [,3] [,4] [,5]
## [1,]  1.0  0.9  0.9  0.9  0.9
## [2,]  0.9  1.0  0.9  0.9  0.9
## [3,]  0.9  0.9  1.0  0.9  0.9
## [4,]  0.9  0.9  0.9  1.0  0.9
## [5,]  0.9  0.9  0.9  0.9  1.0