glmnet (version 1.8-2)

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.

Usage

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

Arguments

object
Fitted "cv.glmnet" 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
...
Not used. Other arguments to predict.

Value

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

Details

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

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 http://www-stat.stanford.edu/~hastie/Papers/glmnet.pdf http://www.jstatsoft.org/v33/i01/

See Also

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

Examples

Run this code
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))

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