logit.reg: Cyclic Coordinate Descent for Logistic regression

In CDLasso: Coordinate Descent Algorithms for Lasso Penalized L1, L2, and 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.

Details

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

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.

Author(s)

Edward Grant, Kenneth Lange, Tong Tong Wu

Maintainer: Edward Grant [e-mail]

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

CDLasso documentation built on May 1, 2019, 8:02 p.m.