Tweedie Distributions

Description

The AIC for Tweedie models

Usage

1
	AICtweedie( glm.obj, k = 2)

Arguments

glm.obj

a fitted Tweedie glm object

k

numeric: the penalty per parameter to be used; the default is k=2

Details

See AIC for more details on the AIC; see dtweedie for more details on computing the Tweedie densities

Value

Returns a numeric value with the corresponding AIC (or BIC, depending on k)

Note

Computing the AIC can take a long time!

Author(s)

Peter Dunn (pdunn2@usc.edu.au)

References

Dunn, P. K. and Smyth, G. K. (2008). Evaluation of Tweedie exponential dispersion model densities by Fourier inversion. Statistics and Computing, 18, 73–86.

Dunn, Peter K and Smyth, Gordon K (2005). Series evaluation of Tweedie exponential dispersion model densities Statistics and Computing, 15(4). 267–280.

Jorgensen, B. (1997). Theory of Dispersion Models. Chapman and Hall, London.

Sakamoto, Y., Ishiguro, M., and Kitagawa G. (1986). Akaike Information Criterion Statistics. D. Reidel Publishing Company.

See Also

AIC

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
library(statmod) # Needed to use  tweedie  family object

### Generate some fictitious data
test.data <- rgamma(n=200, scale=1, shape=1)

### Fit a Tweedie glm and find the AIC
m1 <- glm( test.data~1, family=tweedie(link.power=0, var.power=2) )

### A Tweedie glm with p=2 is equivalent to a gamma glm:
m2 <- glm( test.data~1, family=Gamma(link=log))

### The models are equivalent, so the AIC shoud be the same:
AICtweedie(m1)
AIC(m2)