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Last data update: 2014.03.03

R: Cross-validation for HDtweedie

cv.HDtweedie

R Documentation

Cross-validation for HDtweedie

Description

Does k-fold cross-validation for HDtweedie, produces a plot, and returns a value for lambda. This function is modified based on the cv function from the glmnet package.

Usage

cv.HDtweedie(x, y, group = NULL, p, weights, lambda = NULL, 
	pred.loss = c("deviance", "mae", "mse"), 
	nfolds = 5, foldid, ...)

Arguments

`x`	matrix of predictors, of dimension np*; each row is an observation vector.
`y`	response variable. This argument should be non-negative.
`group`	To apply the grouped lasso, it is a vector of consecutive integers describing the grouping of the coefficients (see example below). To apply the lasso, the user can ignore this argument, and the vector is automatically generated by treating each variable as a group.
`p`	the power used for variance-mean relation of Tweedie model. Default is 1.50.
`weights`	the observation weights. Default is equal weight.
`lambda`	optional user-supplied lambda sequence; default is `NULL`, and `HDtweedie` chooses its own sequence.
`pred.loss`	loss to use for cross-validation error. Valid options are: `"deviance"` Deviance. `"mae"` Mean absolute error. `"mse"` Mean square error. Default is `"deviance"`.
`nfolds`	number of folds - default is 5. Although `nfolds` can be as large as the sample size (leave-one-out CV), it is not recommended for large datasets. Smallest value allowable is `nfolds=3`.
`foldid`	an optional vector of values between 1 and `nfold` identifying what fold each observation is in. If supplied, `nfold` can be missing.
`...`	other arguments that can be passed to HDtweedie.

Details

The function runs HDtweedie nfolds+1 times; the first to get the lambda sequence, and then the remainder to compute the fit with each of the folds omitted. The average error and standard deviation over the folds are computed.

Value

an object of class cv.HDtweedie is returned, which is a list with the ingredients of the cross-validation fit.

`lambda`	the values of `lambda` used in the fits.
`cvm`	the mean cross-validated error - a vector of length `length(lambda)`.
`cvsd`	estimate of standard error of `cvm`.
`cvupper`	upper curve = `cvm+cvsd`.
`cvlower`	lower curve = `cvm-cvsd`.
`name`	a text string indicating type of measure (for plotting purposes).
`HDtweedie.fit`	a fitted `HDtweedie` object for the full data.
`lambda.min`	The optimal value of `lambda` that gives minimum cross validation error `cvm`.
`lambda.1se`	The largest value of `lambda` such that error is within 1 standard error of the minimum.

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) 

# 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)

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/cv.HDtweedie.Rd_%03d_medium.png", width=480, height=480)
> ### Name: cv.HDtweedie
> ### Title: Cross-validation for HDtweedie
> ### Aliases: 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) 
> 
> # 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)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>