Solved – glm.fit: algorithm did not converge -Tweedie

Question

I'm trying to estimate $p$ in tweedie regression, but I got the following message:

glm.fit: algorithm did not converge

I'm using public data from "GLMs for insurance data" book by Piet de Jong, and Gillian Z. Heller. Here is my code:

install.packages("sas7bdat") # A package to read SAS data set
library(sas7bdat)

mydata <- read.sas7bdat("http://www.businessandeconomics.mq.edu.au/our_departments/Applied_Finance_and_Actuarial_Studies/acst_docs/glms_for_insurance_data/data/claims_sas_miner.sas7bdat")
View(mydata) # Viewing the data


library(tweedie)

out=tweedie.profile(mydata$CLM_AMT~1,p.vec=seq(1.1,1.9,length=9),
                    method="interpolation",do.ci=TRUE,do.smooth=TRUE,do.plot=TRUE) # Estimating p

Any idea?

Answer 1

The fit at 1.9 doesn't converge but you don't need it, since it's nowhere near the optimum.

Try

 out=tweedie.profile(mydata$CLM_AMT~1,p.vec=seq(1.1,1.85,length=16),
                 method="interpolation",do.ci=TRUE,do.smooth=TRUE,do.plot=TRUE)

You could probably get it to converge by playing with some of the options (though the likelihood might not change all that much), but it's not worth the trouble.