Thanks Jari. Perhaps I can pose my question slightly differently. In the example I gave using the dune data, there is an effect of management. How would you go about finding where the differences among levels are? In this example its perhaps easy...plot it. But for examples where sites differ substantially so that treatments within sites are still closer to a site centroid than a treatment centroid but consistently shifts in one direction in ordination space? In that sense, using the custom contrasts is a posthoc procedure, because having found a significant effect, I tried to find where that difference originated. I am aware that I only included the first three contrasts, but I thought that would be enough to give the idea of what I was doing. Having done all pairwise contrasts I would correct for the multiple comparisons with Bonferroni or such.
Once you know where the differences are, how would you then go about finding out what is causing the difference? (This is why I was asking about the coefficients) Thanks again, Alan -------------------------------------------------- Email: aghay...@gmail.com Mobile: +41763389128 Skype: aghaynes On 27 May 2013 07:27, Jari Oksanen <jari.oksa...@oulu.fi> wrote: > 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 *** 0.001 ** 0.01 * 0.05 . 0.1 1 > > > > man <- dune.env$Management > > contmat <- cbind(c(1,-1,0,0), c(1,0,-1,0), # construct a new contrast > matrix > > c(1,0,0,-1), c(0,1,-1,0), > > c(0,1,0,-1), c(0,0,1,-1)) > > contrasts(man) <- contmat[,1:4] > > trt1.2 <- model.matrix(~ man)[,2] > > trt1.3 <- model.matrix(~ man)[,3] > > trt1.4 <- model.matrix(~ man)[,4] > > > > ad2 <- adonis(dune ~ trt1.2 + trt1.3 + trt1.4 ) > > # Call: > > # adonis(formula = dune ~ trt1.2 + trt1.3 + trt1.4) > > # > > # Terms added sequentially (first to last) > > # > > # Df SumsOfSqs MeanSqs F.Model R2 Pr(>F) > > # trt1.2 1 0.1483 0.14827 0.8381 0.03449 0.545 > > # trt1.3 1 0.8371 0.83712 4.7321 0.19472 0.001 *** > > # trt1.4 1 0.4832 0.48321 2.7315 0.11240 0.032 * > > # Residuals 16 2.8304 0.17690 0.65839 > > # Total 19 4.2990 1.00000 > > # --- > > # Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1 > > > > > > I was just wondering whether it was fair to say that the species with > high > > coefficients (adonis(...)$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: aghay...@gmail.com > > Mobile: +41763389128 > > Skype: aghaynes > > > > [[alternative HTML version deleted]] > > > > _______________________________________________ > > R-sig-ecology mailing list > > R-sig-ecology@r-project.org > > https://stat.ethz.ch/mailman/listinfo/r-sig-ecology > > -- > Jari Oksanen, Dept Biology, Univ Oulu, 90014 Finland > jari.oksa...@oulu.fi, Ph. +358 400 408593, http://cc.oulu.fi/~jarioksa > > > > [[alternative HTML version deleted]]
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