predict.cv.HDtweedie: make predictions from a "cv.HDtweedie" object.

In emeryyi/hdtweedie: The Lasso for the Tweedie's Compound Poisson Model Using an IRLS-BMD Algorithm

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. (2013), “Tweedie's Compound Poisson Model With Grouped Elastic Net,” submitted to Journal of Computational and Graphical Statistics.

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")

emeryyi/hdtweedie documentation built on May 16, 2019, 5:06 a.m.