logLiktweedie: Tweedie Distributions

Description

The log likelihood for Tweedie models

Usage

logLiktweedie( glm.obj, dispersion=NULL)

Value

Returns the log-likelihood from the specified model

Arguments

glm.obj: a fitted Tweedie glm object
dispersion: the dispersion parameter $ϕ$ ; the default is NULL which means to use an estimate

Author

Peter Dunn (pdunn2@usc.edu.au)

Details

The log-likelihood is computed from the AIC, so see AICtweedie for more details.

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. tools:::Rd_expr_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. tools:::Rd_expr_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.

Examples

Run this code

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)

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