Wow! That was fast! Unfortunately, Agnieszka, I don't think you will find an objective criterion for this. Clearly, species which do not have a statistically significant value are probably less useful, but of the many that are significant, many may be marginal.
Without knowing fully what you are hoping to achieve, I think I would rank the species by indicator value, and establish the highest threshold for indicator value that gives you a suitable number of species for each type. That way, if you are looking to write a field key, for example, you would have sufficient values to identify every type I suspect. Good luck, Dave [EMAIL PROTECTED] wrote: > Hello, > > I was uclear before, I'm sory about it. I forgot to add that I'm using > duleg... > > I used mvpart for multivariate regression trees. My input variables are > environmental parameters, output variables are macrophyte species > (presence=1,absence=0 in conecutive cases=lakes). For obtained classes I used > duleg to find indicator species for every class. I checked the article > Dufrene, > M. and Legendre, P. 1997. Species assemblages and indicator species: the need > for a flexible asymmetrical approach. Ecol. Monogr. 67(3):345-366. The authors > used the threshold of indval=0.25(25%) and that's the only hint I've found in > the literature. This threshod seems to reasonable, but still I have impression > that's too low... > > best regards > Agnieszka > > > >>Agnieszka, > > >> As Jari indicated, it depends on which function you meant in you >>inquiry. The duleg() function implements the Dufrene-Legendre >>algorithm, where "indicator" species are indicative of a priori >>communities. It this requires a classification, and is biased to find >>species which occur in the dataset approximately as often as the mean >>cluster size. > > >> The indpsc() function calculates the mean similarity of all samples >>a species occurs in. This is slightly biased because we know that the >>samples being used to calculate the mean share at least the species that >>defines them, but it is still possible to compare those values to the >>mean similarity of the whole matrix, or to an expectation of maximum >>similarity. Obviously, as species occur more frequently, the harder it >>is to have a really high similarity (indicator value), with the extreme >>case that a species that occurs in every sample must have the same value >>as the mean of the whole matrix. > > >> To tell the truth, I forgot that indspc() was included in the >>current version of labdsv. In the new version (due to be released any >>day), I have included a permutation test that estimates quantiles of >>expected values for different numbers of occurrences. It works, but is >>pretty slow. Jari has created a version that uses parametric statistics >>to estimate the same envelope, but I haven't had a chance to try it yet. > > >> What research are you doing, and what are you really trying to >>determine? Perhaps something altogether different will work better. > > >>Thanks, Dave Roberts > > >>>On Mon, 2005-09-19 at 09:41 +0200, [EMAIL PROTECTED] wrote: >>> >>> >>>>Hi, >>>> >>>>I'm trying to find out what threshold of indicator value in labadsv should >>>>be >>>>used to accept a specie as an indicator one? So far I assumed that >>>>indval=0.5 >>>>is high enough to avoid any mistakes but it was based only in my intuition. >>>> >>>>I'd be greatful for any advise >>>> >>>>best regards >>>> >>> >>> >>>Agnieszka, >>> >>>R mailing list software appends the following to your message: >>> >>> >>> >>>>PLEASE do read the posting guide! >>>>http://www.R-project.org/posting-guide.html >>> >>> >>>Then about indicator value analysis. You should be more specific: there >>>seem to be three alternatives functions for "indicator species" in >>>labdsv. Which did you mean? At least two of these return an item called >>>"indval", and these two alternative "indvals" are very different. For >>>the Dufręne-Legendre indvals, you should check the original paper (see >>>references in the help page), and there you even have an associated "P >>>value". In indspc, the variance of the indval clearly is dependent on >>>species frequency. Moreover, in indspc the expected indval (and its >>>variance) are dependent on the whole set of sites you have: these >>>reflect the general "homogeneity" of your data set. Therefore you cannot >>>say there that any certain value would mean that a species is a good >>>indicator. However, it would be easy to work out standard errors for >>>indspc indvals. >>> >>>I think it would be more useful to post to some other mailing group >>>where people are more concerned about indicator species, or to contact >>>the package author directly (I CC this message to him). >>> >>>cheers, jari oksanen > > > > > > > -- > Best regards, > mailto:[EMAIL PROTECTED] > > Agnieszka Strzelczak, Research Assistant > mailto:[EMAIL PROTECTED] > > Institute of Chemistry and Environmental Protection > Faculty of Chemical Engineering > Szczecin University of Technology > Aleja Piastow 42 > 71-065 Szczecin > Poland > > -- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ David W. Roberts office 406-994-4548 Professor and Head FAX 406-994-3190 Department of Ecology email [EMAIL PROTECTED] Montana State University Bozeman, MT 59717-3460 ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html