I have just realised that I sent this to Per only. For those interested on the list:
-----Original Message----- From: Gygax Lorenz FAT Sent: Tuesday, September 14, 2004 4:35 PM To: 'Per Tor�ng' Subject: RE: [R] glmmPQL and random factors Hi Per, > glmmPQL(Fruit.set~Treat1*Treat2+offset(log10(No.flowers)), > random=~1|Plot, family=poisson, data=...) > > Plot is supposed to be nested in (Treat1*Treat2). > Is this analysis suitable? As far as I understand the methods and with my experience using such analyses, I would say that the model is ok the way you specified it. glmmPQL (and the underlying lme) is so intelligent (a thousand thanks to the developpers!) as to recognise if the treatments are fixed per plot, i.e. only one level of the two treatments appears in each plot. The denominator degrees of freedom in the anova table are adjusted automatically. I.e. your denominator df should be the number of plots minus five, the number of dfs you need for the fixed effects (Treat1, Treat2, the interaction, the covariate and the one df you always loose from the total of observations). > Moreover, what is the meaning of typing > random=~1|Plot compared to random=~Treat1*Treat2|Plot? The first version means, that the intercept / overall mean can vary from plot to plot. I.e. each plot may have another mean due to the fact that it grows somewhere else in addition to the differing treatments. The second version tries to model a difference in reaction to treatment 1 and 2 for each of the plots (which does not make sense in your case as each plot is only subjected to one kind of treatment). In a crossed design, i.e. if you could have treated your plants individually and had all treatment combinations in each of the plots, the first version implies that all the plots react in the same consistent way to the treatments. I.e. that the general level of each plot may be different, but the differences due to treatment are the same in each plot, the reaction of the plots are shifted but have the same shape (this is the same as saying that you only consider main effects of treatment and plot). The second version allows to estimate the reactions for each plot, i.e. in addition to a general shift, the treatments may have (slightly) different effects in each plot. This is the same as saying that you consider interactions between your fixed and random effects. See also the terrific book by Pinheiro & Bates (Mixed Effects Modelling in S and S-Plus, Springer, 2000). Cheers, Lorenz - Lorenz Gygax Tel: +41 (0)52 368 33 84 / [EMAIL PROTECTED] Centre for proper housing of ruminants and pigs Swiss Federal Veterinary Office ______________________________________________ [EMAIL PROTECTED] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
