CDLasso (version 1.1)

logit.reg: Cyclic Coordinate Descent for Logistic regression

Description

Cyclic Coordinate Descent for Logistic regression with p predictors and n cases

Usage

logit.reg(X, Y, lambda = 1)

Arguments

X

p x n design matrix - Note that the rows of X correspond to predictors and the columns to cases.

Y

Outcome of length n

lambda

Penalization Parameter. For optimal lambda, use cv.logit.reg.

Value

X

The design matrix.

cases

The number of cases

predictors

The number of predictors

lambda

The value of penalization parameter lambda used.

residual

A vector of length p listing 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.

Details

logit.reg performs an algorithm for estimating regression coefficients in a penalized logistic regression model. The algorithm is based on cyclic coordinate descent.

References

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.

See Also

print.logit.reg

summary.logit.reg

cv.logit.reg

plot.cv.logit.reg

l1.reg

Examples

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