Qiu, Weiyu <weiyu <at> ualberta.ca> writes: > > Hi, > > I'm doing a generalized linear mixed model, and I currently use an > R function called "glmm". However, in > this function they use a standard normal distribution for the random > effect, which doesn't satisfy my > case, i.e. my random effect also follows a normal distribution, > but observations over time are somehow > correlated, so the covariance matrix would be different the > identity matrix. Besides, it's also > different from the commonly used AR(1), AR(2) structures, so > actually I need a way to write down the > covariance matrix for the random effect in GLMM by myself. >
If you could get by with an AR or ARMA structure then you could use lme() with the 'correlation' argument from the nlme package. If you have enough data/are willing to fit a completely unstructured correlation matrix, you could use corSymm. See ?corStruct in the nlme package documentation: it is *in principle* possible to write functions to implement your own correlation structures, e.g. see corSymm nlme:::corSymm.corMatrix nlme:::coef.corNatural However, this will not be easy unless you are already a good R programmer and familiar with the nlme package. If you really need to do this I definitely recommend that you buy and study Pinheiro and Bates's 2000 book. It might also be worth taking a look at the cor* objects in the "ape" package, which are coded slightly more transparently. Followups should probably go to r-sig-mixed-mod...@r-project.org good luck Ben Bolker ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.