glmnet (version 2.0-5)

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

"predict"(object, newx, s=c("lambda.1se","lambda.min"),...)
"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 is numeric, it is taken as the value(s) of lambda to be used.

...

Not used. Other arguments to predict.

Value

... 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

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))