[R] adonis (vegan package) and subsetted factors

[Gavin Simpson]

On Thu, 2008-04-10 at 11:18 -0300, tyler wrote:
> Hi,
> 
> I'm trying to use adonis on a subset of data from a dataframe. The
> actual data is in columns 5:118, and the first four columns are various
> factors. There are 3 levels of the factor Habitat, and I want to examine
> differences among only two of them. So I started with:

Hi Tyler,

This behaviour arises from the following, using the in-built dune data:

> example(dune)

dune> data(dune)

dune> data(dune.env)
> newdune.env <- subset(dune.env, Management != "NM")
> newdune.env$Management
 [1] BF SF SF SF HF SF HF HF BF BF HF SF SF HF
Levels: BF HF NM SF

Notice this hasn't dropped the empty level "NM", and this is what is
catching out adonis --- it is not checking for empty levels in the
grouping factor, as this shows:

> newdune <- dune[which(dune.env$Management != "NM"), ]
> adonis(newdune ~ Management*A1, data=newdune.env, permutations=100)

Call:
adonis(formula = newdune ~ Management * A1, data = newdune.env,
permutations = 100) 

                    Df SumsOfSqs  MeanSqs  F.Model     R2 Pr(>F)    
Management     3.00000   0.57288  0.19096  1.27735 0.2694  <0.01 ***
A1             1.00000   0.29851  0.29851  1.99672 0.1404   0.33    
Management:A1  3.00000   0.35831  0.11944  0.79892 0.1685   0.87    
Residuals      6.00000   0.89699  0.14950          0.4218           
Total         13.00000   2.12669                   1.0000           
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

For now, forcibly remove empty factor levels as per your second example,
but I'll take a look at fixing adonis() --- it looks easy but that could
be deceptive! I've CC'd the maintainer (Jari Oksanen) here as well, as I
don't think Jari follows R-help too closely at the moment.

<snip/>
> 
> Thanks,
> 
> Tyler
> 
> ps. Sorry for not supplying a reproducible bit of code. The data.frame
> is quite large. The general layout is:

It is often instructional to use one of the in-built data sets (as I
have here), even if just to prove to yourself that it isn't a problem
with your data.

All the best,

G

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