HDtweedie (version 1.2)

coef.HDtweedie: 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 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.

Value

The coefficients at the requested values for lambda.

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.

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

# NOT RUN {
# 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))
# }