tweedie (version 2.3.5)

# AICtweedie: Tweedie Distributions

## Description

The AIC for Tweedie glms

## Usage

AICtweedie( glm.obj, dispersion=NULL, k = 2, verbose=TRUE)

## Value

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

## Arguments

glm.obj

a fitted Tweedie glm object

dispersion

the dispersion parameter $$\phi$$; the default is NULL which means to use an estimate

k

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

verbose

if TRUE (the default), a warning message is produced about the Poisson case; see the second Note below

## Author

Peter Dunn (pdunn2@usc.edu.au)

## Details

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

## 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.

AIC

## 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:
{"ft_lang_label":"__label__en","ft_lang_prob":0.5493713,"math_prob":0.795609,"size":489,"snap":"2023-40-2023-50","text_gpt3_token_len":149,"char_repetition_ratio":0.12577319,"word_repetition_ratio":0.0,"special_character_ratio":0.3006135,"punctuation_ratio":0.12871288,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.9731341,"pos_list":[0],"im_url_duplicate_count":[null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2023-12-07T23:31:00Z\",\"WARC-Record-ID\":\"<urn:uuid:80d6b8f0-7366-4334-8efd-049f55e7e4de>\",\"Content-Length\":\"27596\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:7a455731-13f0-49a6-9738-404eba2cde91>\",\"WARC-Concurrent-To\":\"<urn:uuid:624daca1-7d39-4b21-b08f-f7bda6ebe4f5>\",\"WARC-IP-Address\":\"52.85.132.10\",\"WARC-Target-URI\":\"https://www.rdocumentation.org/packages/tweedie/versions/2.3.5/topics/AICtweedie\",\"WARC-Payload-Digest\":\"sha1:IJOHDI3DDSXG7EZX6GVK5IGTPGCOWPJ4\",\"WARC-Block-Digest\":\"sha1:I2DVN76T36RTCMM6DYE4XJQ3HVDO7VEY\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2023/CC-MAIN-2023-50/CC-MAIN-2023-50_segments_1700679100705.19_warc_CC-MAIN-20231207221604-20231208011604-00278.warc.gz\"}"}