glmnet: predict.cv.glmnet – R documentation

Become an expert in R — Interactive courses, Cheat Sheets, certificates and more!

predict.cv.glmnet

make predictions from a "cv.glmnet" object.

Description

This function makes predictions from a cross-validated glmnet model, using the stored "glmnet.fit" object, and the optimal value chosen for lambda (and gamma for a 'relaxed' fit.

Usage

## S3 method for class 'cv.glmnet'
predict(object, newx, s = c("lambda.1se", "lambda.min"), ...)

## S3 method for class 'cv.relaxed'
predict(
  object,
  newx,
  s = c("lambda.1se", "lambda.min"),
  gamma = c("gamma.1se", "gamma.min"),
  ...
)

Arguments

`object`	Fitted `"cv.glmnet"` or `"cv.relaxed"` object.
`newx`	Matrix of new values for `x` at which predictions are to be made. Must be a matrix; can be sparse as in `Matrix` package. See documentation for `predict.glmnet`.
`s`	Value(s) of the penalty parameter `lambda` at which predictions are required. Default is the value `s="lambda.1se"` stored on the CV `object`. Alternatively `s="lambda.min"` can be used. If `s` is numeric, it is taken as the value(s) of `lambda` to be used. (For historical reasons we use the symbol 's' rather than 'lambda' to reference this parameter)
`...`	Not used. Other arguments to predict.
`gamma`	Value (single) of 'gamma' at which predictions are to be made

Details

This function makes it easier to use the results of cross-validation to make a prediction.

Value

The object returned depends on the ... argument which is passed on to the predict method for glmnet objects.

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, Journal of Statistical Software, Vol. 33, Issue 1, Feb 2010
https://www.jstatsoft.org/v33/i01/ https://arxiv.org/abs/1707.08692
Hastie, T., Tibshirani, Robert, Tibshirani, Ryan (2019) Extended Comparisons of Best Subset Selection, Forward Stepwise Selection, and the Lasso

Examples

x = matrix(rnorm(100 * 20), 100, 20)
y = rnorm(100)
cv.fit = cv.glmnet(x, y)
predict(cv.fit, newx = x[1:5, ])
coef(cv.fit)
coef(cv.fit, s = "lambda.min")
predict(cv.fit, newx = x[1:5, ], s = c(0.001, 0.002))
cv.fitr = cv.glmnet(x, y, relax = TRUE)
predict(cv.fit, newx = x[1:5, ])
coef(cv.fit)
coef(cv.fit, s = "lambda.min", gamma = "gamma.min")
predict(cv.fit, newx = x[1:5, ], s = c(0.001, 0.002), gamma = "gamma.min")

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