Hi everyone,

I am trying to analyse my data from a small plant experiment (for a meeting tomorrow afternoon) and am a beginner to R so I apologise if this is a very basic question.

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)
- 3 replicates of each treatment (blocks labelled 1-3)

I need to see whether I have a significant block effect and as it will be a random effect, I need to use lme in R.

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 combinations for species B.
I have final biomass data for each of the plants.

My first step is to check species A for interspecific competition, for which I will use:

Response variable:  Amix (continuous - biomass measurement)
Random effect:      Block (factor - replicates labelled as 1,2,3)
Main effect:          watering treatment (wt) (factor, 1-7)
Covariate:             Aiso (continuous - biomass measurement)
Covariate:             Amix.initialsize (initial biomass of Amix to account for any size variation before treatment was started)

Before I thought I'd have to include block as random effect, I used the following formula for a lm:
lm1<-lm(Amix~Aiso+wt+block+Amix.initialsize+Aiso:wt)

but I do not know how to structure this in an lme. Would someone please help me with this?

Also, what is the difference between an lme and an lmer as I am unsure which one to use,


Thank you,

Sarah Buckmaster
E-mail: s.buckmaster.08@aberdeen.ac.uk

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