coef.HDtweedie: get coefficients or make coefficient predictions from an...

In HDtweedie: The Lasso for Tweedie's Compound Poisson Model Using an IRLS-BMD Algorithm

get coefficients or make coefficient predictions from an "HDtweedie" object.

Description

Computes the coefficients at the requested values for lambda from a fitted HDtweedie object.

Usage

## S3 method for class 'HDtweedie'
coef(object, s = NULL, ...)

Arguments

object	fitted HDtweedie model object.
s	value(s) of the penalty parameter lambda at which predictions are required. Default is the entire sequence used to create the model.
...	not used. Other arguments to predict.

Details

s is the new vector at which predictions are requested. If s is not in the lambda sequence used for fitting the model, the coef function will use linear interpolation to make predictions. The new values are interpolated using a fraction of coefficients from both left and right lambda indices.

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.

See Also

predict.HDtweedie method

Examples

# load HDtweedie library
library(HDtweedie)

# load data set
data(auto)

# fit the lasso
m0 <- HDtweedie(x=auto$x,y=auto$y,p=1.5)

# the coefficients at lambda = 0.01
coef(m0,s=0.01)

# define group index
group1 <- c(rep(1,5),rep(2,7),rep(3,4),rep(4:14,each=3),15:21)

# fit grouped lasso
m1 <- HDtweedie(x=auto$x,y=auto$y,group=group1,p=1.5)

# the coefficients at lambda = 0.01 and 0.04
coef(m1,s=c(0.01,0.04))

HDtweedie documentation built on May 10, 2022, 9:06 a.m.