coef.cv.HDtweedie {HDtweedie}

get coefficients or make coefficient predictions from a "cv.HDtweedie" object.

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

Friedman, J., Hastie, T., and Tibshirani, R. (2010), "Regularization paths for generalized linear models via coordinate descent," Journal of Statistical Software, 33, 1.

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)