l2.reg {CDLasso} | R Documentation |
Cyclic Coordinate Descent for L2 regression with p predictors and n cases
l2.reg(X, Y, lambda = 1)
X |
|
Y |
Outcome of length |
lambda |
Penalization Parameter. For optimal |
l2.reg
performs an algorithm for estimating regression coefficients in a penalized L2 regression model. The algorithm is based on cyclic coordinate descent. For the new L1 algorithm that is faster, see (l1.reg).
X |
The design matrix. |
cases |
The number of cases |
predictors |
The number of predictors |
lambda |
The value of penalization parameter |
residual |
A vector of length |
L2 |
The sum of the residuals |
estimate |
The estimate of the coefficients |
nonzeros |
The number "selected" variables included in the model. |
selected |
The name of the "selected" variables included in the model. |
Edward Grant, Kenneth Lange, Tong Tong Wu
Maintainer: Edward Grant edward.m.grant@gmail.com
Wu, T.T. and Lange, K. (2008). Coordinate Descent Algorithms for Lasso Penalized Regression. Annals of Applied Statistics, Volume 2, No 1, 224-244.
set.seed(100) n=500 p=2000 nzfixed = c(1:5) true.beta<-rep(0,p) true.beta[nzfixed] = c(1,1,1,1,1) x=matrix(rnorm(n*p),p,n) y = t(x) %*% true.beta rownames(x)<-1:nrow(x) colnames(x)<-1:ncol(x) #Lasso penalized L2 regression out<-l2.reg(x,y,lambda=2) #Re-estimate parameters without penalization out2<-l2.reg(x[out$selected,],y,lambda=0) out2