I know that normally the point of beta diversity tests is to tell you that two groups are significantly dissimilar (in around about hypothesis rejection way) but I have the issue where I want to say two groups are significantly SIMILAR. I have a PERMANOVA result pasted below where two groups seem to be more similar to each other (C and carnivore) than the other two groups based on the distances and the F values. On the PCOA plot it is also apparent. However, I want to quantitively state that they are. The two options I see are ranking the distance values given by the test to show that two groups have significantly lower distances than the rest OR to compare the F values and show that two groups have significantly lower pseudo F values when comparing one to the other (basically comparing effect sizes).
My question is, do any of these two methods work to show significant "similarity" or is there a better test out there?
For the equivalence trial, the null hypothesis is that |Mean1 – Mean2| >= d, where d is
a prespecified threshold equal to the largest difference that is still considered to be ‘‘clinically meaningless.’’ Deciding on this threshold (d) is a difficult and critically important aspect of the equivalence design. It serves as the operational definition of ‘‘the same as.’’ The alternative hypothesis is that |Mean1 – Mean| < d, or that the confidence interval for the difference between the means falls within the prespecified threshold.
I'm interesting to see if we can get a statistician to 'qiime-in' on this question.
Thanks for the input. Most of my research is constantly trying to show differences so that article is very relevant for my efforts in this case. However, I guess in my case my groups are all "significantly different" and I want to show that two of them are less "significantly different" than the rest. Or more simply put, how can I show that they are closer together on the darn PCOA plot in a quantitative way ? Does comparing the F values from the Permanova test suffice?
Thanks,
Sam
I wanted to show that the starting and ending days were self-similar, but the largest difference was between the starting and ending days.
So I graphed the distance matrix as a heatmap. Brighter colors show greater differences, and you see that the start-to-end is much larger than start-to-start or end-to-end, because it's
Here, I'm reporting the ADONIS test, but you could also do another comparison of means to show their difference. This is pretty much what diversity beta-group-significance does!
Yes, pseudo-F value is a measure of effect size, just like the F-stistic. linklink
The PERMANOVA test statistic, pseudo-F, is modeled directly after the conventional ANOVA F-statistic. It is calculated in the same way, as a ratio of the amount of variation between versus within groups, with the numerator and denominator each weighted by their degrees of freedom. It is 0 or positive, with larger values corresponding to larger proportional importance of the grouping factor. ...
An important, and reassuring, point: the [sdeudo-F] test statistic is identical to the conventional F-statistic when calculated using Euclidean distance for a single variable.
As long as the data meets the test's assumptions, you are good to go!
