tweedie (version 2.0.2)

AICtweedie: Tweedie Distributions

Description

The AIC for Tweedie models

Usage

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 $k=2$

Value

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

Note

Computing the AIC can take a long time!

Details

See AIC for more details on the AIC; see link{dtweedie} for ore details on computing the Tweedie densities

References

Dunn, P. K. & 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

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)

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