# predict.cv.glmnet

0th

Percentile

##### 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.

Keywords
models, regression
##### 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.
##### Details

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

##### Value

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

glmnet, and print, and coef methods, and cv.glmnet.
library(glmnet) 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))