[R-sig-ME] Using lme (possibly nlme) in R

[Ben Bolker]

  She posted to r-help as well, I gave her basic advice (either lme or
lmer will work for this problem, basic syntax, consider treating blocks
as fixed, or possibly using aov(), since there are only 3 blocks) and
expect that she will reply to r-sig-mixed-models when she hits the next
roadblock ...

  Ben

Douglas Bates wrote:
> Sarah sent this directly to me and I am pretty much wiped out right
> now having finished the third short course in a month and preparing or
> helping to prepare three other presentations.  Could someone else take
> a stab at answering this?  I'm tired and I have a full day of
> international travel ahead of me tomorrow and I would probably give
> nonsense advice.  It looks as if it is urgent for her.
> 
> 
> On Thu, Jul 23, 2009 at 11:28 AM, <s.buckmaster.08 at aberdeen.ac.uk> wrote:
>> I am an MSc Ecology student in Scotland, UK and am struggling to analyse my dissertation data in R - I only have today and tomorrow to understand my output and so was hoping you could offer some advice. I would be really grateful!
>>
>> I carried out a plant experiment examining plant interactions between two species (A and B) under different watering treatments. I had
>> - 7 watering treatments (7 different watering frequencies labelled 1-7) ...and...
>> - 3 replicates of each treatment
>>
>> At each watering treatment, I had 5 different combinations of plants. I have shown how I have labelled these in brackets for species A:
>> A in isolation (Aiso)
>> A+A monoculture (Amono)
>> A+B interspecific competition (Amix)
>> .. and the same for species B.
>>
>> The first step is to check species A for interspecific competition.... I need to see whether I have a significant replicate effect (block effect) and as this will be a random effect, I am going to use a lme. I have the following factors:
>>
>> Response variable:  Amix
>> Random effect:      Block (replicates labelled as 1,2,3)
>> Main effect:          watering treatment (wt) (1-7)
>> Covariate:             Aiso
>> Covariate:             Amix.initialsize (initial biomass of Amix to account for any size variation before treatment was started)
>>
>> I used following formula in lm (before I thought I'd have to include block as random effect):
>> lm1<-lm(Amix~Aiso+wt+block+Amix.initialsize+Aiso:wt)
>>
>> but do not know how to structure it in an lme. How would I do this?
>> Also, what is the difference between an lme and an lmer as I was unsure which one to use,
>>
>>
>> Thank you,
>>
>> Sarah Buckmaster
>> E-mail: s.buckmaster.08 at aberdeen.ac.uk
>>
> 
> _______________________________________________
> R-sig-mixed-models at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models


-- 
Ben Bolker
Associate professor, Biology Dep't, Univ. of Florida
bolker at ufl.edu / www.zoology.ufl.edu/bolker
GPG key: www.zoology.ufl.edu/bolker/benbolker-publickey.asc