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This function makes predictions from a cross-validated glmnet model,
using the stored "glmnet.fit" object, and the optimal value
chosen for lambda.
# S3 method for cv.glmnet
predict(object, newx, s=c("lambda.1se","lambda.min"),...)
# S3 method for cv.glmnet
coef(object,s=c("lambda.1se","lambda.min"),...)Fitted "cv.glmnet" object.
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.
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.
The object returned depends the … argument which is passed on
to the predict method for glmnet objects.
This function makes it easier to use the results of cross-validation to make a prediction.
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.jstatsoft.org/v33/i01/
glmnet, and print, and coef methods, and cv.glmnet.
# 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))
# }
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