[R-sig-ME] about: GLMM for continuous response

[Aslıhan Şentürk Acar]

Dear Sir,

I am a Phd student working on GLMM implementation for continuous 
response of insurance claim size. I have some problems to implement GLMM 
in R. I would be very glad if you could answer.

Response variable y_ij: jth claim amount of individual i. People can 
have just one claim or more than one claim (max. claim number is 136. so 
j=1,2,...,136).

There are 22 000 individuals in the sample. Explanatory variables are 
(fixed effects): claim number in one year (integer), age(integer), 
gender (0-1,factor), provience (0-6,factor), package (0-3,factor), 
marital status (0-1,factor).

*I use only random intercept as random effect.

I am using R 3.0.3, MASS, lme4,nlme and lm4.0 packages.

My questions are:


1- I use GLMMPQL (MASS package) that generally converges. But laplace 
and quadrature never converges. I did not understand the reason. What 
can be the reason? While implementing GLMM are the individuals that have 
just one claim problem for model convergence?

note: gamma glm, log transform and linear mixed model, gamma GEE all 
converges.

2- I simulated gamma distributed response variable and used a few 
covariates to test convergence error. Laplace converges, Quadrature does 
not converge again. While PQL gives 0.2632854 for standard deviation of 
random intercept, glmer (laplace) gives std. dev 1028.16. Why is so huge 
difference between two methods?

3- GLMMPQL converges with all explanatory variables. Province and gender 
are not significant. When I exclude only province GLMMPQL does not 
converge. But when I exclude gender and both province+gender it 
converges. Why is this contradiction?

4- I could not see any paper on GLMM applications for continuous 
response. I am afraid of doing something wrong but I ca not find any 
reference. Can you give me any references to understand detail of GLMM 
implementation for continuous response and general idea for 
implementation of GLMM.

5- I do not want to make logarithmic transform and fit linear mixed 
models, I want to use data on original scale. Do you offer any other 
distribution exempt gamma GLMM?


I appreciate any help.

Thank you very much.

Best wishes,

Aslihan


-- 
Aslihan Senturk Acar

Research Assistant
Hacettepe University
Department of Actuarial Sciences
Beytepe, Ankara
08600

Phone: (+90) 312 297 6160 / 119
Email: aslihans at hacettepe.edu.tr