prsLasso: Auxiliary function which returns the objective, penalty, and...

In smoothedLasso: A Framework to Smooth L1 Penalized Regression Operators using Nesterov Smoothing

Description

Auxiliary function which returns the objective, penalty, and dependence structure among regression coefficients of the Lasso for polygenic risk scores (prs).

Usage

prsLasso(X, y, s, lambda)

Arguments

X	The design matrix.
y	The response vector.
s	The shrinkage parameter used to regularize the design matrix.
lambda	The regularization parameter of the prs Lasso.

Value

A list with six functions, precisely the objective u, penalty v, and dependence structure w, as well as their derivatives du, dv, and dw.

References

Mak, T.S., Porsch, R.M., Choi, S.W., Zhou, X., and Sham, P.C. (2017). Polygenic scores via penalized regression on summary statistics. Genet Epidemiol, 41(6):469-480.

Mak, T.S. and Porsch, R.M. (2020). lassosum: LASSO with summary statistics and a reference panel. R package version 0.4.5.

Hahn, G., Lutz, S., Laha, N., and Lange, C. (2020). A framework to efficiently smooth L1 penalties for linear regression. bioRxiv:2020.09.17.301788.

Examples

library(smoothedLasso)
n <- 100
p <- 500
betavector <- runif(p)
X <- matrix(runif(n*p),nrow=n,ncol=p)
y <- X %*% betavector
s <- 0.5
lambda <- 1
temp <- prsLasso(X,y,s,lambda)

smoothedLasso documentation built on March 21, 2021, 9:07 a.m.