Seth <sjmyers <at> syr.edu> writes: > I would like to specify a spherical correlation structure for spatially > autocorrelated residuals in a model based upon the logistic function of a > response that is a proportion (0 to 1) (so usual binary logistic regression > is not an option). There is no need for a g-side random effect with > grouping in this model. Am I correct that nlme requires this (meaning a > correlated error structure only is not permissible)? I have tried to > replicate the 'abuse' of the lme function I've seen for similar problems > (specifying that all observations belong to one group), but this does not > seem to work for nlme. Any legitimate work arounds?
The proportion part might be a bit tricky (you can only reasonably assume that the variation is normally distributed if the variance is pretty small), but I think gnls() is what you're looking for if you want non-linear least squares with correlation and/or heteroscedasticity but without random effects. 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.
