Hi Chris, > I am trying to fit the following linear model to logged per capita > fecundity data (ie number of babies per female) for a mouse: > > RsNRlS <- glm(formula = ln.fecundity ~ summer.rainfall + N + > lagged.rainfall + season, …) > > I am using this relationship in a simulation model, and the current > statistical model I have fit is unsatisfactory. The problem is I get a > global estimate of variance (MSE), but I think it varies across subsets > of the data. Specifically, seasons when there is lots of reproduction > (e.g. fall) tend to have high variance, while seasons with little > reproduction (e.g. summer) have small amounts of variance. I am > looking for a method for estimating the coefficients in my linear > model, and estimating a separate error for subsets of the data (ie for > each of the 4 seasons). The end goal is to take this linear model back > into my simulation model to make predictions about fecundity, but with > separate variance terms for subsets of the data.
Are you using glm because you need a specific distribution family (such like poisson)? If not, you could possibly use gls with the argument weights= varFixed (~ season) With that you estimate your parameters and at the same time you allow for (and estimate) the different variances for the season. If you need the poisson distribution, I am not quite sure what to do. Perhaps glm also accepts this weight argument or perhaps you need to work with a generalised procedure of lme (either from one of the new lme packages or from MASS). Regards, Lorenz - Lorenz Gygax, Dr. sc. nat. Tel: +41 (0)52 368 33 84 / [EMAIL PROTECTED] Center for proper housing of ruminants and pigs Swiss Veterinary Office agroscope FAT Tänikon, CH-8356 Ettenhausen / Switzerland Fax : +41 (0)52 365 11 90 / Tel: +41 (0)52 368 31 31 ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html