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Last data update: 2014.03.03

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

predict.cv.HDtweedie

R Documentation

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 <weiqian@stat.umn.edu>

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.

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")

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(HDtweedie)
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/HDtweedie/predict.cv.HDtweedie.Rd_%03d_medium.png", width=480, height=480)
> ### Name: predict.cv.HDtweedie
> ### Title: make predictions from a "cv.HDtweedie" object.
> ### Aliases: predict.cv.HDtweedie
> ### Keywords: models regression
> 
> ### ** 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")
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>