Dear Brett, I have the same problem. I found several approaches in the literature, bbut non efficient or clear review... well there were some, but too mathematic for me as a simple biologist. By what I know, it is complicated to work with ratios (which have difficult statistical properties). On the other way, you also have the problem of colinearity between variables (I imagine). I found some approaches to solve this, but none was universal or definitive. There is an article by Leonart et al. that proposes a simple formula, but it has been much discussed, and a statistical lecturer told me that it is not recent. On the other way, in Ade4 lab, I saw in the other day that they standartise the columns with the mean. I tred this, and it was very good... gave much clearer results. My supervisor said to use PCA, as it is and simply consider that the first component is 'size'... however this did not gave clear images of the data... thus I am as traped in the beggining. I suppose in the end all this hypothesis are possible and correct, and most will give very similar answers.
I am also puzzled by the range of multivariate techniques, that give similar answers... particularly because in many cases different authors (and statistical packages) call the same techniques with different names, which really messes the things. I started to do a summary of it (which I can send you), of information I found in several books... as well, in the end, as I saw it now, things are much simpler, and mainly consist in a couple of method with variations, which arises different names. On the other way, people from the R list have discussed a lot stepwise analysis, and some do not recommend it at all... so some care should be taken in this point as well. Anyway, I can adive you of a free online manual from the VEGAN package (from www.R-project.org) which for me was very good and compares many methods using the same data: http://cc.oulu.fi/%7Ejarioksa/opetus/metodi/index.html hope this helps somehow, or at least shows solidarity with your question ;-) Please let me know if if you finally find 'a' answer :-) Best wishes, Marta Quoting [EMAIL PROTECTED]: > Dear morphometrician, > > I have recently reviewed 3 genera of catsharks that display a great deal of > morphological conservation within the genera, however, there is also > prominent sexual dimorphism present (profoundly so in some species). There is > quite a bit of shape variation between juveniles and adults, in one genus in > particular, but I think that the shape variation is being obscured by the > size component. > > I have a sizeable morphometric data set (# measures >> # taxa & specimens) > and have used principal components analysis on the raw data to explore shape > variation within each of the genera (not between). The first component was > always a general component and accounted for more than 85-90% of the > variation in most instances, therefore the bipolar components only > contributed relatively little to the overall shape variation resulting in > crowded PCA plots. > > The main reference I have used for the analyses to date has been 'Pimental. > 1979. Morphometrics. The multivariate analysis of biological data' however, > it doesn't deal with size correction. Can anyone suggest a review that deals > with size correction, or can I convert my data to ratios and then log > transform the data? > > I am also looking for reviews of canonical discriminant functions analysis > and stepwise discriminant function analysis in an attempt to quantitate > differences between species within a genus. > > Thanks for your help. > > Brett > > ************************************ > Brett Human > Shark Researcher > 27 Southern Ave > West Beach SA 5024 > Australia > 61 8 8356 6891 > [EMAIL PROTECTED] > ************************************ > > > > == > Replies will be sent to list. > For more information see http://life.bio.sunysb.edu/morph/morphmet.html. > ------------------------------------------------- This mail sent through IMP: http://horde.org/imp/ == Replies will be sent to list. For more information see http://life.bio.sunysb.edu/morph/morphmet.html.
