logLiktweedie {tweedie} | R Documentation |
Tweedie Distributions
Description
The log likelihood for Tweedie models
Usage
logLiktweedie( glm.obj, dispersion=NULL)
Arguments
glm.obj |
a fitted Tweedie |
dispersion |
the dispersion parameter |
Details
The log-likelihood is computed from the AIC,
so see AICtweedie
for more details.
Value
Returns the log-likelihood from the specified model
Note
Computing the log-likelihood may take a long time.
Note
Tweedie distributions with the index parameter as 1
correspond to Poisson distributions when \phi = 1
.
However,
in general a Tweedie distribution with an index parameter equal to one
may not be referring to a Poisson distribution with \phi=1
,
so we cannot assume that \phi=1
just because the index parameter is set to one.
If the Poisson distribution is intended,
then dispersion=1
should be specified.
The same argument applies for similar situations.
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. doi: 10.1007/s11222-007-9039-6
Dunn, Peter K and Smyth, Gordon K (2005). Series evaluation of Tweedie exponential dispersion model densities Statistics and Computing, 15(4). 267–280. doi: 10.1007/s11222-005-4070-y
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
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:
logLiktweedie(m1)
logLik(m2)