Hi David & Rune

Sorry if I have not used the correct way to respond but I only get the digest so I'm not sure how to do it properly.

Anyway, to the point.

The only way I know how to fit cumulative link functions for mixed-model polytomous ordered logistic (or probit, complementary log-log) links using the GLM fitting algorithm is to use composite link functions as originally published for multinomial data by Thompson and Baker (1981) Applied Statistics 30, 125-131. The generalisation to mixed models using GLMMs is given ("buried in") in Candy 1997 Biometrics 53:146-160 (see pg 152) and uses a Poisson distn with constraints imposed on fitted values to sum to observed marginal frequencies. This composite link function approach is a bit messy to set up, so it might be less trouble to program it directly as simply an nlme problem. Also if the categories are not ordered this approach breaks down because its not possible to build the appropriate constraints into the GLMM fitting algorithm.

HTH, Steve

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Steven G Candy
Senior Applied Statistician
Southern Ocean Ecosystems
Australian Antarctic Division
Kingston, Tasmania 7050
Australia
ph +61 (0)3 6232 3135




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