Nested design and F-, p- & R²-values in eHOF package

Question

I would like to consider my nested study design in the HOF function of the eHOF package. We sampled in points which where nested in sample blocks and those where nested in villages (random).

So far I used GLMM to describe the effect of habitat type(Habitat, explanatory, fixed) on species richness (SpR, response variable, fixed). Therefore I used lme (of nlme-package) where I could incorporate the nested study design in the function:

model1<-lme(SpR~Habitat,random=~1|Village/Block,data=birds) # full model

Now I would like to describe the effect on SpR along a gradient. My HOF function looks like this:

hof.blocks<-HOF(SpR,Indexc,M=max(SpR),bootstrap=100,
                 family=poisson)

Is there a way to incorporate the nested study design in HOF?

Also I was not able to obtain P-values, nor F- or R2-values. Did I overlook something?

Package: eHOF Version: 1.4 R version 3.0.2

Answer 1

No, not at the moment. You could try to fit the nonlinear models by hand using nlme(), perhaps looking in the eHOF package code for hints on how to specify the formula etc, but that package uses a non-linear optimiser directly, and not nlme() so you'll need to do the translation work to get a model formula that nlme can use and work out other details.