In dajmcdon/cplr: Compressed and penalized linear regression

knitr::opts_chunk$set(
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R-package cplr

This package implements the simulations and data analysis examples for compressed and penalized linear regression as in (Homrighausen and McDonald, 2017). Essentially, the design matrix is premultiplied by a sparse matrix, reducing the number of available observations from $n$ to $q$. However, the addition of a ridge penalty results in estimates of the true coefficient vector with lower mean-squared error, even relative to ridge regression (in some cases). The result is improved computation and better statistical accuracy.

Installation

1. If not already installed, use

install.packages("devtools")

1. Install the current version of this package with

devtools::install_github("dajmcdon/cplr")

Minimal example

library(cplr)

n = 100
p = 5
q = 50
X = generateX(n, diag(1,p), 'rnorm')
Y = generateY(X, p:1, 'rnorm')
out = compressedRidge(X, Y, 'convexComb', q=q, lam.max=10)
plot(out,xlab=bquote(lambda),ylab="",las=1,bty='n',lty=1,ylim=c(0,5.5),
     main='Coefficient trace for\n Convex Combination compression', family='serif')
mtext(bquote(widehat(beta)), 2, line=2, las=1)

dajmcdon/cplr documentation built on May 14, 2019, 3:29 p.m.