
Hello everybody,

 

I wonder if anyone can help me with specifying a right model for my analysis. I am a beginner to lme methods and I difficulty understand the nuances of the numerous possibilities from the on-line helps.

I took different measures on birds in 4 sites with 2 different forest treatments (2 sites per treatments). Thus I have a nested design where site is nested in treatment.

Additionally I have an unbalanced design (different number of birds capture pet sites and per treatments), and I have heteroscedasticity in my responses variable between my treatments. Moreover my sample size is not huge: 35 individual in total.

 

I suppose that my model should be something like:

mod<-lme(measure1~treatment, random=~1|site, data=x, weights = varIdent(form=~1|treatment), method=”REML”)

Anova (mod, type=”III”)

 

I use the nlme package in R version 2.13.1. My mains questions are:

1) I have to put “sites” in random and take in count that it is nested in treatments. So should I write it: random = ~1|site   or   random = ~1|treatment/site ?

2) The use of the function weights=varIdent allows me to specify that I have a case of heteroscedasticity, but I don’t get if I have to write it: varIdent(form = ~1|treatment)   or   varIdent(form = ~1|site)   or   varIdent(form = ~site|treatment)   ?

3) I didn’t get if I have the best method function. Most of people said that method = “REML” is the best because unbiased but in all examples that I saw they used method = “ML”. According to the method, my p-value change. Thus which one should I take?

4) After that I would like to add several other parameters like age for example in fixed effect (young or old). Will it change something in the model or its reliability?

 

My questions are little “naïve”, sorry for that, but I couldn’t get the Pinheiro and Bates (2000) book and I am not fluent in statistics.

Just in case this is an example of my data:





treatment



site



measure1



age





T



M20



13792



old





T



L5



14192



old





C



PERK



15424



old





C



G5



28112



old





C



G5



36592



young





T



L5



42192



young





T



M20



65856



young





C



G5



66064



old





T



L5



66288



young





C



PERK



73664



old





 

Thank a lot for your help.

 

   Claire


	[[alternative HTML version deleted]]

