[cc'ing back to r-sig-ecology] [Please keep sending replies to r-sig-ecology so that others may benefit from the conversation, and so that others can answer if I can't or am too busy (!)]
I don't know what you mean by the "degree of fit to your data". Are you trying to do a goodness-of-fit test? The standard deviance-based test for goodness of fit/overdispersion that you may be thinking of (e.g. see if residual deviance/residual df approx. 1, or test residual deviance against a chi-squared distribution with df=(residual df)) only applies to NON-overdispersed models. You might want to get the book by Zuur et al on mixed models in ecology. Ben Bolker On Fri, Jul 30, 2010 at 11:49 AM, Javier Martinez <javi.martinez.lo...@gmail.com> wrote: > Hello again Mr. Bolker, > > I have now tried glmmPQL and look very promising because I have in > fact the expected results. Since I do not get a deviance parameter > from these models I cannot assess their degree of fit to my data, so I > was thinking if it would be possible to assess it somehow by doing a > linear fit model between the expected and fitted values from the > resulting glmmPQL model. Does it make sense to you? > > Thank you very much for any advise and regards, > > Javier > > On Thu, Jul 29, 2010 at 6:25 PM, Ben Bolker <bbol...@gmail.com> wrote: >> A little more information would probably be helpful. Here's what >> I'm guessing: >> >> You have no 'treatment' except the passage of time, and only two >> time points (say, before/after). You have a total of 16 measurements >> (2 each at 8 sites), they are like binomial data (number of counts of >> type x out of a total number N counted) but overdispersed. You want >> to test whether the proportion of type x changed between 'before' and >> 'after'. If the data were normally distributed, you could use a paired >> t-test. >> >> Is that a correct description? >> >> If so, then time should be treated as a fixed factor, group as random. >> 8 samples is probably enough (just). >> >> If your counts are fairly large (i.e. the minimum of the numbers >> of 'successes' and 'failures' in a typical group is >5) then you could >> safely use glmmPQL in the MASS package: >> >> glmmPQL(cbind(successes,failures)~time,random=~1|group, >> family="quasibinomial",data=...) >> >> Have you thought about simply using a nonparametric test on the >> proportions (i.e. wilcox.test(prop.before, prop.after,paired=TRUE) ... ?) >> >> On Thu, Jul 29, 2010 at 12:07 PM, Javier Martinez >> <javi.martinez.lo...@gmail.com> wrote: >>> Thanks to all of you! I did know the e-mail by Bates, which is out of >>> my understanding, but I did not know the wiki on mixed models and the >>> manuscript by Bolker! My data are based on 2 temporal samples from 8 >>> different sites. I use mixed models because I want to avoid >>> pseudo-replication including the grouping factor into my model and >>> thus looking for the trends within each group and not looking at the >>> data as if they were independent. The question is, can I really use a >>> mixed model if I only have two cases per group? At the end there are >>> 16 cases in the regression plot but I am not sure if such a grouped >>> analysis is right! >>> >>> Thank you again for your help! >>> >>> Javier >>> >>> On Wed, Jul 28, 2010 at 6:44 PM, Javier Martinez >>> <javi.martinez.lo...@gmail.com> wrote: >>>> Dear R-users, >>>> >>>> I am using the 'lmer' function from package 'lme4', looking for a >>>> regression model which takes into account the grouped nature of my >>>> data. I am using frequencies as the dependent variable and percentages >>>> as the independent one. After some reading I think I should use the >>>> 'quasibinomial' family because there is 'overdispersion' in my data >>>> set (greater residual deviance than residual degrees of freedom). So, >>>> I test this regression model but I do not get a significance p-value >>>> for the regression! I have to test many different regressions with >>>> different data, so how can I assess the significance of each of of >>>> them? >>>> >>>> Thank you very much for your help! >>>> >>>> Javier >>>> >>> >> > _______________________________________________ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
