# glmnet-package

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##### Elastic net model paths for some generalized linear models

This package fits lasso and elastic-net model paths for regression, logistic and multinomial regression using coordinate descent. The algorithm is extremely fast, and exploits sparsity in the input x matrix where it exists. A variety of predictions can be made from the fitted models.

Keywords
package, models, regression
##### Details

ll{ Package: glmnet Type: Package Version: 1.0 Date: 2008-05-14 License: What license is it under? } Very simple to use. Accepts x,y data for regression models, and produces the regularization path over a grid of values for the tuning parameter lambda. Only 5 functions: glmnet predict.glmnet plot.glmnet print.glmnet coef.glmnet

##### References

Friedman, J., Hastie, T. and Tibshirani, R. (2008) Regularization Paths for Generalized Linear Models via Coordinate Descent

##### Aliases
• glmnet-package
##### Examples
x=matrix(rnorm(100*20),100,20)
y=rnorm(100)
g2=sample(1:2,100,replace=TRUE)
g4=sample(1:4,100,replace=TRUE)
fit1=glmnet(x,y)
predict(fit1,newx=x[1:5,],s=c(0.01,0.005))
predict(fit1,type="coef")
plot(fit1,xvar="lambda")
fit2=glmnet(x,g2,family="binomial")
predict(fit2,type="response",newx=x[2:5,])
predict(fit2,type="nonzero")
fit3=glmnet(x,g4,family="multinomial")
predict(fit3,newx=x[1:3,],type="response",s=0.01)
Documentation reproduced from package glmnet, version 1.1-5, License: GPL-2

### Community examples

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