predict.HDtweedie: make predictions from a "HDtweedie" object.

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

make predictions from a "HDtweedie" object.

Description

Similar to other predict methods, this functions predicts fitted values from a HDtweedie object.

Usage

## S3 method for class 'HDtweedie'
predict(object, newx, s = NULL,
type=c("response","link"), ...)

Arguments

object	fitted HDtweedie model object.
newx	matrix of new values for x at which predictions are to be made. Must be a matrix.
s	value(s) of the penalty parameter lambda at which predictions are required. Default is the entire sequence used to create the model.
type	type of prediction required: Type "response" gives the mean response estimate. Type "link" gives the estimate for log mean response.
...	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 predict function will use linear interpolation to make predictions. The new values are interpolated using a fraction of predicted values from both left and right lambda indices.

Value

The object returned depends on type.

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

coef method

Examples

# load HDtweedie library
library(HDtweedie)

# load auto data set
data(auto)

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

# predicted mean response at x[10,]
print(predict(m0,type="response",newx=auto$x[10,]))

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

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

# predicted the log mean response at x[1:5,]
print(predict(m1,type="link",newx=auto$x[1:5,]))

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