EDiLD/vegan — comment on commit 1208c9c54264ed30c21dabb60caeb7151515a510
File:  line 0
By jarioksa:

We have prc() in vegan, and mvcchart() should be represented with the same example data (pyrifos). The data set itself only contains the community data, but you can generate the environmental data using example(pyrifos) or commands in its example (or in ?prc). These data seems to offer the examples for Anderson & Thompson style analyses. There are twelve sampling sites (ditches) which have been studied at eleven time points each. The two first dates are four and one week before Pyrifos admission and provide an example for comparing changes against a fixed baseline (the third observation is soon after Pyrifos admission so that there are not much effects yet). All points can be used in moving baseline where samples are compared against average of previous observations.

I admit that these data highlight one obvious problem in these control charts: they assume the time series is stationary. These data come from Dutch ditches which are strongly seasonal so that also the control sites (no Pyrifos) will change. However, this kind of situation is normal and it should be shown how the method handles such cases. Obviously, the confidence limits will be inflated when seasons are mixed even with a ditch.

This brings as a natural choice a third alternative baseline: the control plots (no Pyrifos) of the same sampling occasion. This would be similar to prc() analysis in spirit.

The pyrifos data are constructed as a single rectangular data set with 132 rows for 12 ditches times 11 sampling times. To be able to handle this kind of data, the mvcchart should have arguments

sites: a factor identifying a site
time: a numerical variable ordering the sampling times

Then it needs information about baseline that could be 'time' or 'fixed' sites. If there would be a third (new) alternative, then it should have information on 'treatment' that is used as baseline (which in this case would be the average of four control ditches with no Pyrifos, 'dose = 0').

After first quick reading, I could not figure out how Anderson & Thompson found the confidence limits. They write about "bootstrap", but I don't know how to do that with dissimilarities in the strict sense (i.e., resampling residuals and adding them to fitted values). Perhaps they actually had permutations that are easy.

BTW, I have started to implement Cao dissimilarity index in a private branch because of this Anderson & Thompson paper. C code is done but untested and R interface and documentation are still lacking. I'll push it to GitHub as it is ready.