[R-sig-eco] adonis

[Jari Oksanen]

Alan,

A few comments on your procedure. You have two non-standard things in your message: you try to do something that looks like post hoc tests, and you use non-standard contrasts. There is nothing post hoc in your post hoc tests. What you do is that you break your factor variable into separate contrasts. If do so, you should carefully read the adonis output which says

"Terms added sequentially (first to last)"

If your contrasts are correlated, like they are in the example you gave, the results for individual terms will depend on the order of terms. Usually people associate post hoc tests with multiple testing problem, but there is nothing about that in the example you gave. It is just simple testing of individual contrasts.

Second point is that you used non-standard contrasts. The species coefficients will depend on contrasts and therefore they change. There are easier way of doing the same. For instance, you seem to want to have sum contrasts, but with different baseline level. Check functions like model.matrix, contrasts, relevel, and as.data.frame. However, the magnitude of coefficient also depends on specific contrasts that you use.

Cheers, Jari Oksanen

On 24/05/2013, at 16:48 PM, Alan Haynes wrote:

> Hi all,
> 
> Im using adonis for some plant community analysis and have been following
> theBioBucket example of how to posthoc tests (
> http://thebiobucket.blogspot.ch/2011/08/two-way-permanova-adonis-with-custom.html
> )
> 
> 
> 
> data(dune)
> data(dune.env)
> ad1 <- adonis(dune ~ Management, data=dune.env, permutations=99)
> # Call:
> # adonis(formula = dune ~ Management, data = dune.env, permutations = 99)
> #
> # Terms added sequentially (first to last)
> #
> # Df SumsOfSqs MeanSqs F.Model      R2 Pr(>F)
> # Management  3    1.4686 0.48953  2.7672 0.34161   0.01 **
> # Residuals  16    2.8304 0.17690         0.65839
> # Total      19    4.2990                 1.00000
> # ---
> #      Signif. codes:  0 Œ***‚ 0.001 Œ**‚ 0.01 Œ*‚ 0.05 Œ.‚ 0.1 Œ ‚ 1
> 
> man <- dune.env$Management
> contmat <- cbind(c(1,-1,0,0), c(1,0,-1,0), # construct a new contrast matrix
>                 c(1,0,0,-1), c(0,1,-1,0),
>                 c(0,1,0,-1), c(0,0,1,-1))
> contrasts(man) <- contmat[,1:4]
> trt1.2 <- model.matrix(~ man)[,2]
> trt1.3 <- model.matrix(~ man)[,3]
> trt1.4 <- model.matrix(~ man)[,4]
> 
> ad2 <- adonis(dune ~ trt1.2 + trt1.3 + trt1.4 )
> # Call:
> #      adonis(formula = dune ~ trt1.2 + trt1.3 + trt1.4)
> #
> # Terms added sequentially (first to last)
> #
> # Df SumsOfSqs MeanSqs F.Model      R2 Pr(>F)
> # trt1.2     1    0.1483 0.14827  0.8381 0.03449  0.545
> # trt1.3     1    0.8371 0.83712  4.7321 0.19472  0.001 ***
> # trt1.4     1    0.4832 0.48321  2.7315 0.11240  0.032 *
> # Residuals 16    2.8304 0.17690         0.65839
> # Total     19    4.2990                 1.00000
> # ---
> # Signif. codes:  0 Œ***‚ 0.001 Œ**‚ 0.01 Œ*‚ 0.05 Œ.‚ 0.1 Œ ‚ 1
> 
> 
> I was just wondering whether it was fair to say that the species with high
> coefficients (adonis(...)$coefficients) were the ones causing that
> difference?
> 
> ad2$coefficients[3,abs(ad$coefficients[3,])>1]
> # Elepal    Poapra    Salrep    Poatri    Elyrep    Lolper    Alogen
> # -1.091667  1.975000 -1.375000  3.283333  1.333333  3.000000  1.650000
> 
> If so, would it be better to take the coefficients from the original model
> or the model used for the contrast, as these yield different results:
> 
> ad1$coefficients[3,abs(ad1$coefficients[3,])>1]
> # Rumace   Tripra   Poatri   Plalan
> # 2.316667 1.350000 1.516667 1.541667
> 
> 
> Cheers,
> 
> Alan
> 
> 
> --------------------------------------------------
> Email: aghaynes at gmail.com
> Mobile: +41763389128
> Skype: aghaynes
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-- 
Jari Oksanen, Dept Biology, Univ Oulu, 90014 Finland
jari.oksanen at oulu.fi, Ph. +358 400 408593, http://cc.oulu.fi/~jarioksa