Description Usage Arguments Value References Examples
View source: R/smoothedLasso.r
Auxiliary function to define the objective function of an L1 penalized regression operator.
1 | objFunction(betavector, u, v, w)
|
betavector |
The vector of regression coefficients. |
u |
The function encoding the objective of the regression operator. |
v |
The function encoding the penalty of the regression operator. |
w |
The function encoding the dependence structure among the regression coefficients. |
The value of the L1 penalized regression operator for the input betavector.
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.
1 2 3 4 5 6 7 8 9 | library(smoothedLasso)
n <- 100
p <- 500
betavector <- runif(p)
X <- matrix(runif(n*p),nrow=n,ncol=p)
y <- X %*% betavector
lambda <- 1
temp <- standardLasso(X,y,lambda)
print(objFunction(betavector,temp$u,temp$v,temp$w))
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