predict.cv.HDtweedie {HDtweedie}

make predictions from a "cv.HDtweedie" object.

Description

This function makes predictions from a cross-validated HDtweedie model, using the stored "cv.HDtweedie" object, and the optimal value chosen for lambda.

Usage

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

Arguments

object

fitted cv.HDtweedie object.

newx

matrix of new values for x at which predictions are to be made. Must be a matrix. See documentation for predict.HDtweedie.

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

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

Author(s)

Wei Qian, Yi Yang and Hui Zou
Maintainer: Wei Qian <[e-mail]>

References

Qian, W., Yang, Y., Yang, Y. and Zou, H. (2016), “Tweedie's Compound Poisson Model With Grouped Elastic Net,” Journal of Computational and Graphical Statistics, 25, 606-625.

See Also

cv.HDtweedie, and coef.cv.HDtweedie methods.

Examples

# load HDtweedie library
library(HDtweedie)

# load data set
data(auto)

# 5-fold cross validation using the lasso
cv0 <- cv.HDtweedie(x=auto$x,y=auto$y,p=1.5,nfolds=5) 

# predicted mean response at lambda = lambda.1se, newx = x[1,]
pre = predict(cv0, newx = auto$x[1,], type = "response")

# define group index
group1 <- c(rep(1,5),rep(2,7),rep(3,4),rep(4:14,each=3),15:21)

# 5-fold cross validation using the grouped lasso 
cv1 <- cv.HDtweedie(x=auto$x,y=auto$y,group=group1,p=1.5,nfolds=5)

# predicted the log mean response at lambda = lambda.min, x[1:5,]
pre = predict(cv1, newx = auto$x[1:5,], s = cv1$lambda.min, type = "link")