On Mon, Jan 4, 2010 at 6:24 AM, Walmes Zeviani <walmeszevi...@hotmail.com> wrote:
> AD Hayward wrote: >> >> Dear all, >> >> I'm attempting to use a piecewise regression to model the trajectory >> of reproductive traits with age in a longitudinal data set using a >> mixed model framework. The aim is to find three slopes and two points- >> the slope from low performance in early age to a point of high >> performance in middle age, the slope (may be 0) of the plateau from >> the start of high performance to the end of high performance , and the >> slope of the decline from the end of high performance to the end of >> life. >> >> I've found the segmented package useful, but it cannot be implemented >> in a mixed model framework. I've also attempted piecewise regression >> using this formula in lmer: >> >> m<-lmer(repro ~ OTHER FIXED EFFECTS + age*(age < 2) + age*(age >= 2 & >> age < 8) + age*(age >= 8) + (1|id) + (1|yr), data = reproduction, >> family = binomial, link = "logit", GHQ = TRUE) >> >> However, this gives the warning: >> >> Warning message: >> In mer_finalize(ans) : gr cannot be computed at initial par (65) >> >> which is not apparent if I use just two break points or I implement >> the model in glm. >> >> My question is essentially whether anyone can recommend a method for >> performing piecewise regression in lmer or another mixed model >> framework. Any advice would be greatly appreciated. > Adam, > > A segmented linear model, for estimation purposes, is a nonlinear model. It > requires a iteractive procedure for estimation of fixed effects. You could > use nlmer() for this. It appears that Adam is using fixed knot positions, in which case the segmented model is a linear model. He is also using family = binomial so it is a generalized linear mixed model, which does require iterative optimization, but does not require nlmer(). ______________________________________________ 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.
