glmnet-package {glmnet} | R Documentation |
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.
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
Jerome Friedman, Trevor Hastie and Rob Tibshirani
Maintainer: Trevor Hastie <hastie@stanford.edu>
Friedman, J., Hastie, T. and Tibshirani, R. (2008)
Regularization Paths for Generalized Linear Models via Coordinate
Descent, https://web.stanford.edu/~hastie/Papers/glmnet.pdf
Journal of Statistical Software, Vol. 33(1), 1-22 Feb 2010
http://www.jstatsoft.org/v33/i01/
Simon, N., Friedman, J., Hastie, T., Tibshirani, R. (2011)
Regularization Paths for Cox's Proportional Hazards Model via
Coordinate Descent, Journal of Statistical Software, Vol. 39(5)
1-13
http://www.jstatsoft.org/v39/i05/
Tibshirani, Robert., Bien, J., Friedman, J.,Hastie, T.,Simon,
N.,Taylor, J. and Tibshirani, Ryan. (2012)
Strong Rules for Discarding Predictors in Lasso-type Problems,
JRSSB, vol 74,
http://statweb.stanford.edu/~tibs/ftp/strong.pdf
Stanford Statistics Technical Report
Glmnet Vignette https://web.stanford.edu/~hastie/glmnet/glmnet_alpha.html
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)