logit.reg {CDLasso} R Documentation

## 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.

### 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 edward.m.grant@gmail.com

### 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.

`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

```

[Package CDLasso version 1.1 Index]