logit.reg {CDLasso} | R Documentation |
Cyclic Coordinate Descent for Logistic regression with p predictors and n cases
logit.reg(X, Y, lambda = 1)
X |
|
Y |
Outcome of length |
lambda |
Penalization Parameter. For optimal |
logit.reg
performs an algorithm for estimating regression coefficients in a penalized logistic regression model. The algorithm is based on cyclic coordinate descent.
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 |
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., Chen, Y.F., Hastie, T., Sobel E. and Lange, K. (2009). Genome-wide association analysis by lasso penalized logistic regression. Bioinformatics, Volume 25, No 6, 714-721.
set.seed(1001) n=500;p=5000 beta=c(1,1,1,1,1,rep(0,p-5)) x=matrix(rnorm(n*p),p,n) xb = t(x) %*% beta logity=exp(xb)/(1+exp(xb)) y=rbinom(n=length(logity),prob=logity,size=1) rownames(x)<-1:nrow(x) colnames(x)<-1:ncol(x) #Lasso penalized logistic regression using optimal lambda out<-logit.reg(x,y,50) print(out) #Re-estimate parameters without penalization out2<-logit.reg(x[out$selected,],y,0) out2