# predict.cv.glmnet

##### make predictions from a "cv.glmnet" object.

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.

- Keywords
- models, regression

##### Usage

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

##### 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

```
# NOT RUN {
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")
# }
```

*Documentation reproduced from package glmnet, version 3.0-2, License: GPL-2*