coef.cv.HDtweedie: get coefficients or make coefficient predictions from a...

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

Description

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

Usage

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

Arguments

object	fitted cv.HDtweedie object.
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, it is the largest value of lambda such that error is within 1 standard error of the minimum. Alternatively s="lambda.min" can be used, it is the optimal value of lambda that gives minimum cross validation error cvm. 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 get coefficients or make coefficient predictions.

Value

The coefficients at the requested values for lambda.

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.

Friedman, J., Hastie, T., and Tibshirani, R. (2010), "Regularization paths for generalized linear models via coordinate descent," Journal of Statistical Software, 33, 1.
http://www.jstatsoft.org/v33/i01/

See Also

cv.HDtweedie, and predict.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)

# the coefficients at lambda = lambda.1se
coef(cv0)

# 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)

# the coefficients at lambda = lambda.min
coef(cv1, s = cv1$lambda.min)

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