I wish to analyze the effect of tillage, nitrogen and cover crops on weed communities. The design can be described as the following:
- 4 blocks
- main plot factor: tillage (nested in blocks)
- sub plot factor: nitrogen (nested in tillage)
- sub sub plot factor: cover crops (nested in nitrogen)
Hence, in a classical univariate mixed model (lme syntax), the error structure would be defined as (1|block/tillage/nitrogen) if only one sample is present at the sub sub plot level.
Now, I wish to analyse in R the effects of tillage*nitrogen*cover crops (all simple effects, all second order interactions and the third order interaction) on weed communities through partial CCA (to filter out the block effect).
I believe the following CCA model is correct (in {vegan}):
cca2013=cca(log(cover_2013+1)~tillage*N*CC+Condition(block),data=cover_2013)
And from what I gathered on the {permute} vignette, the permutation design should be defined the following way:
h
Hi Guillaume Adeux,
I don't think your model structure is accounting for the type of permutation you want. I recommend you read Winkler et al (2015) to make sure which effects you really want to test using your experimental design. Once you decided, you can build a small randomization script to build the null models for your hypothesis.
An alternative is to explore the function nested.anova.dbrda() in the BiodiversityR package.
I hope this helps.
Cheers
Winkler AM, Webster MA, Vidaurre D, Nichols TE, Smith SM (2015) Multi-level block permutation. Neuroimage 123:253–268. the Condition() terms your are using to test your model is working properly
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