It is unlikely that your request will be answered faster if you post it a second time in exactly the same way ...
> Suppose I have a linear mixed-effects model (from the package > nlme) with nested random effects (see below); how would I present > the results from the random effects part in a publication? > > Specifically, I´d like to know: > (1) What is the total variance of the random effects at each level? > Random effects: > Formula: ~ 1 | plotcode > (Intercept) > StdDev: 0.04176364 > > Formula: ~ 1 | treatment %in% plotcode > (Intercept) Residual > StdDev: 0.08660458 0.00833387 What is wrong with an estimted StdDev on the level of plotcode of 0.0418 and on the level of treatment (which is actually the same as to say that this is a block of plants within plotcode that received the same treatment?) of 0.087? > (2) How can I test the significance of the variance components? Calculate a model with an without the component of interest and compare the models using anova(). Perhaps you should read Pinheiro & Bates (2000)? > (3) Is there something like an "r squared" for the whole > model which I can state? None is provided and I do not know how easily it could be calculated. But the use of the measure can be questioned. It is an absolute measure on how much of the variability in the data is explained. Imagine you had true replicates (i.e. several measurements under absolutely identical situations). Imagine further that these measurements do nevertheless show some variability. If this variability was substantial, your r-squared would be low even though your model might pick up all the structure that you can find in the data. Thus you can only get as good as 'natural' variability in your data which is not considered by the r-squared measure. Thus, (corrected) r-squared values might tell you something if you compare different models based on the same data (in a similar way as the AIC and BIC criteria) but not if you compare completely different data sets. Regards, Lorenz - Lorenz Gygax, Dr. sc. nat. Centre for proper housing of ruminants and pigs Swiss Federal Veterinary Office agroscope FAT Tänikon, CH-8356 Ettenhausen / Switzerland ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
