R: make predictions from a "cv.glmnet" object.

predict.cv.glmnet {glmnet} | R Documentation

## 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

### See Also

`glmnet`, and `print`, and `coef` methods, and `cv.glmnet`.

### 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")

[Package glmnet version 4.1 Index]