Post this on the r-sig-mixed=models list rather than here. However, fwiw, it is nonsense to estimate a random effect with a sample size of 3. That's trying to estimate variance with a sample size of 3. You can't do it with any meaningful precision. Whether or not the effect really **is** conceptually random is beside the point. I suggest you cross off your list of statistical advisers anyone who says otherwise.
Entropy cannot be denied! -- Bert On Wed, Mar 21, 2012 at 11:01 AM, Lívia Dorneles Audino <livia.aud...@gmail.com> wrote: > Hi everyone! > > > > I have some doubts about mixed effect models and I hope someone could help > me. I´m trying to analyze a dataset coming from samples of dung beetles in > the same forest fragments along 3 consecutive years (1994, 1995 and 1996) > and 14 years after (2010). I sampled dung beetles in 18 different fragments > with different sizes and different degrees of isolation. My aim is to > determine whether total species richness change over time in forest > fragments and to verify the influence of fragment size and isolation on > species richness. However, I'm trying to find a way to consider in the > analyses the temporal pseudo-replication in the data. So, I decided to use > mixed effects models to analyze this data, but I still have doubts about > how I should construct the models. When I asked for some help I received > three different answers about how to construct the model. > > > The first suggestion was to treat year as a fixed rather than a random > effect because it won't be practical to estimate the variance of a > random effect > with only four levels. So, I constructed the model like cited below: > > m1<-lmer(riqueza~área*ano+isolamento*ano(1|fragmento),family=poisson > > > The second suggestion proposed to treat year as a random effect, as cited > bellow: > > m1<-lmer(riqueza~área*ano+isolamento*ano(ano|fragmento),family=poisson > > > And the third suggestion also proposed to treat year as a random effect, > but to consider it *as continuous variable rather than categorical*. In the > models above I consider year as a categorical variable. > > m1<-lmer(riqueza~área*ano+isolamento*ano(ano|fragmento),family=poisson > > > When I analyze my dataset using the second and the third model I always > face with a singular convergence warning: *In mer finalize(ans): singular > convergence (7)**.* What is that mean? Does anyone have some idea about > how can I solve this issue? > > > > I also need to know which one of these models is more appropriate to the > dataset available. Does anyone have some suggestions? > > Thanks in advance! > > Lívia. > > [[alternative HTML version deleted]] > > > ______________________________________________ > 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. > -- Bert Gunter Genentech Nonclinical Biostatistics Internal Contact Info: Phone: 467-7374 Website: http://pharmadevelopment.roche.com/index/pdb/pdb-functional-groups/pdb-biostatistics/pdb-ncb-home.htm ______________________________________________ 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.