Alternatives to lmer
On 17/01/2011 11:22 a.m., Iker Vaquero Alba wrote:
Let me check that I am interpreting your data correctly. It seems that you have 12 sites at each of which you have a number of mated pairs. You have physical measurements on the male and the female of each pair and some environmental measurements on the sites. Yes, that's absolutely correct. The pairs are numbered within sites but some numbers are missing. Might they be the numbers corresponding to pairs that failed to fledge? I think maybe they should be there as well with nfledge = 0. Actually, the missing numbers correspond to the rows I have removed from the original data set because they contained "nfledge" missing values. 'instotal' and 'weatherpc1' are measured at the 'site' level, so the natural way to incorporate them in a model would be through a (instotal + weatherpc1|site) term. Would it be (instotal+weatherpc1|site) or (instotal|site+weatherpc1|site)? According to the way you've written it, only "weatherpc1" seems to be in relation to "site".
In a model formula term ( .... |site) everything in the .... part is at the site level.
Surely also 'nfledge' should be at the 'pair' level ? I think it would then be better to re-shape the data so that each row is a pair and, for example, 'tlength' splits into two covariates 'tlengthM' and 'tlengthF'. I see the point, but what would be the difference compared to having one individual per row and sex as a factor, as it is now?
Well 'nfledge' depends only on the pair. The units at which the response variable is measured must be the underlying units of the analysis.
It looks doubtful to me that a Poisson model will fit this data well, with or without the addition of the nfledge = 0 pairs. Yes, the problem clearly seems to be in teh fact that it's a Poisson model, as assuming Gaussian errors it doesn't return any error message.
But that does not mean that the analysis is correct, even in an approximate way. I do not think your model formula reflects the structure of the data, even if it runs without computing errors. Murray
I think that you should be talking more to some local statisticians before you attempt to fit models to this data. Regards, Murray Thank you so, so much for all your suggestions. I will work on it and tell you if it worked. Best wishes, Iker On 17/01/2011 9:15 a.m., Iker Vaquero Alba wrote:
> > Hello: > > As I posted several days ago, I was trying to implement this model: > >
fledgecoltailmodel1<-lmer(nfledge~sex*briventral*inslarge*weatherpc1*tlength-sex:briventral:inslarge:weatherpc1:tlength-sex:briventral:inslarge:weatherpc1-sex:briventral:inslarge:tlength-sex:briventral:weatherpc1:tlength-sex:inslarge:weatherpc1:tlength-briventral:inslarge:weatherpc1:tlength-sex:briventral:inslarge-sex:briventral:weatherpc1-sex:briventral:tlength-sex:inslarge:weatherpc1-sex:inslarge:tlength-sex:weatherpc1:tlength-briventral:inslarge:weatherpc1-briventral:inslarge:tlength-briventral:weatherpc1:tlength-inslarge:weatherpc1:tlength+(site|pair),family=poisson)
> > but I got the error message: > > Error in asMethod(object) : matrix is not symmetric [1,2] > > as no one seems to know what could be the reason for that or how to
find a solution, I was thinking that maybe I could try using another function. Starting with the one which seems more similar to "lmer", I tried with "GLMM", which I read in some post it could be taken from "lme4" package. However, when I try to use it (either "glmm" or "GLMM"), R tells me such function doesn't exist.
> > Do you know why this could be happening? Do you know of any other
functions I could use to fit my model? I was thinking as well of "MCMCglmm", but I'm not sure I can apply it to my model and I don't think I'm expert enough as to deal with its syntax or the overdispersion problems.
> > I am using R 2.12.0 > > Thank you very much for your help! > > > > Iker Vaquero-Alba > > Centre for Ecology > and Conservation > > Daphne du Maurier > Building > > University of Exeter, > Cornwall Campus > > Treliever Road > > TR10 9EZ Penryn > > U.K. > > > > > > > > [[alternative HTML version deleted]] > > > > > _______________________________________________ > R-sig-mixed-models at r-project.org
<compose?to=R-sig-mixed-models at r-project.org> mailing list
-- Dr Murray Jorgensen http://www.stats.waikato.ac.nz/Staff/maj.html Department of Statistics, University of Waikato, Hamilton, New Zealand Email: maj at waikato.ac.nz <compose?to=maj at waikato.ac.nz> majorgensen at ihug.co.nz <compose?to=majorgensen at ihug.co.nz> Fax 7 838 4155 Phone +64 7 838 4773 wk Home +64 7 825 0441 Mobile 021 0200 8
Dr Murray Jorgensen http://www.stats.waikato.ac.nz/Staff/maj.html Department of Statistics, University of Waikato, Hamilton, New Zealand Email: maj at waikato.ac.nz majorgensen at ihug.co.nz Fax 7 838 4155 Phone +64 7 838 4773 wk Home +64 7 825 0441 Mobile 021 0200 8350