crossvalidation {smoothedLasso} | R Documentation |
Perform cross validation to select the regularization parameter.
crossvalidation(auxfun, X, y, param, K = 10)
auxfun |
A complete fitting function which takes as arguments a data matrix |
X |
The design matrix. |
y |
The response vector. |
param |
A vector of regularization parameters which are to be evaluated via cross validation. |
K |
The number of folds for cross validation (should divide the number of rows of |
A vector of average errors over all folds. The entries in the returned vector correspond to the entries in the vector param
in the same order.
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.
Tibshirani, R. (2013). Model selection and validation 1: Cross-validation. https://www.stat.cmu.edu/~ryantibs/datamining/lectures/18-val1.pdf
library(smoothedLasso)
n <- 1000
p <- 100
betavector <- runif(p)
X <- matrix(runif(n*p),nrow=n,ncol=p)
y <- X %*% betavector
auxfun <- function(X,y,lambda) {
temp <- standardLasso(X,y,lambda)
obj <- function(z) objFunction(z,temp$u,temp$v,temp$w)
objgrad <- function(z) objFunctionGradient(z,temp$w,temp$du,temp$dv,temp$dw)
return(minimizeFunction(p,obj,objgrad))
}
lambdaVector <- seq(0,1,by=0.1)
print(crossvalidation(auxfun,X,y,lambdaVector,10))