glmnet: glmnet-package – R documentation

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glmnet-package

Elastic net model paths for some generalized linear models

Description

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.

Details

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

Author(s)

Jerome Friedman, Trevor Hastie and Rob Tibshirani
Maintainer: Trevor Hastie hastie@stanford.edu

References

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
https://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
https://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,
https://statweb.stanford.edu/~tibs/ftp/strong.pdf
Glmnet webpage with four vignettes https://glmnet.stanford.edu

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)

glmnet

Lasso and Elastic-Net Regularized Generalized Linear Models

v4.1-1

GPL-2

Authors

Jerome Friedman [aut], Trevor Hastie [aut, cre], Rob Tibshirani [aut], Balasubramanian Narasimhan [aut], Kenneth Tay [aut], Noah Simon [aut], Junyang Qian [ctb]

Initial release

2021-02-17