prsLasso {smoothedLasso} | R Documentation |
Auxiliary function which returns the objective, penalty, and dependence structure among regression coefficients of the Lasso for polygenic risk scores (prs).
prsLasso(X, y, s, lambda)
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. |
A list with six functions, precisely the objective u
, penalty v
, and dependence structure w
, as well as their derivatives du
, dv
, and dw
.
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.
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)