Ok, thanks a lot for the explanation! We are going to work on getting it running and see if it makes sense for us to continue with it. Really appreciate the input. Good paper here to walk me through it. Thanks so much!
On Fri, Mar 14, 2014 at 2:18 PM, Paolo Piras <[email protected]>wrote: > > > > If the block of ecological variables for the first morphological group > contains the same TYPE of variables ( i.e. temperature, humidity and other > stuff) of the ecological block of variables related to the second > morphological group you can just work with regresssion after checking > for collinearity among predictors (do a PCA if you find high VIF); PLS can > handle collinearities. > > In this case, the ecology is treated as the independent and morphology > as the dependent (take a look to this: > > http://www.ncbi.nlm.nih.gov/pubmed/20656037 > > to discriminate between PLS regression and PLS correlation, being the > first asymmetrical, the second symmetrical) > > In this case you can run a MANCOVA with morphology as multivariate Y, > ecology as multiple X (i.e. a meaningful number of PLS scores or PCA scores > or the original variables) with "radiation" as factor variable. As I doubt > these data can respect parametric assumptions you can run adonis() that is > non-parametric. > > > BUT (I write this even if it seems to me that it is not the case) ...if > the ecological variables related to the SECOND morphological group are > not of the same TYPE of the ecological variables related to the FIRST > morphological group .....things are a little bit more complicated: ASSUMING > THAT MORPHOLOGY IS REPRESENTED BY THE SAME TYPE OF VARIABLES..... you can > extract vectors of two separate PLS and then relate them to check if they > covary; in this case however, I expect a covariance between the ecological > variables related to the FIRST morphological group and those related to > the FIRST morphological group. > > > The various tests can be much more complex however. > > best > > paolo > > > > > > ------------------------------ > *Da:* [email protected] <[email protected]> per conto di Eliot > Miller <[email protected]> > *Inviato:* venerdì 14 marzo 2014 17.57 > *A:* Paolo Piras > *Cc:* [email protected]; [email protected] > *Oggetto:* Re: [R-sig-eco] Comparing results of two CCAs > > Sure, thanks for the chance. > > I am interested in whether two separate evolutionary radiations have > followed the same ecomorphological trajectory. Are the same morphological > features associated with the same ecological features? I think so, but I > also think in one of the radiations that the group is doing more > ecologically with a more conserved (less variable) morphology. In other > words, increases along certain morphological axes correspond to the same > ecologies in both datasets, but smaller morphological changes in one > dataset are associated with equally large changes in ecology. I want a > method that can get at that. > > Cheers, > Eliot > > > On Fri, Mar 14, 2014 at 11:02 AM, Paolo Piras <[email protected]>wrote: > >> PLS can be performed in pls package while varpart in vegan package >> >> however...could you explain a little bit better the specific hypothesis >> you want to test? >> >> >> Different methods are suited in dependence of the explicit hypothesis >> you set. >> >> >> >> >> >> >> >> >> >> >> ------------------------------ >> *Da:* [email protected] <[email protected]> per conto di Eliot >> Miller <[email protected]> >> *Inviato:* venerdì 14 marzo 2014 15.19 >> *A:* Paolo Piras; [email protected] >> *Cc:* [email protected] >> *Oggetto:* Re: [R-sig-eco] Comparing results of two CCAs >> >> The partial least squares sounds really promising, thanks. I now need >> to go read about and try some tests with it. If you or anyone else has a >> preferred implementation of this in R I'd be interested in hearing about it! >> >> Alain--can you elaborate on how I might be able to use variance >> partitioning? I haven't used either of these methods before, but reading >> about it it sounds intended to quantify the amounts of variation in a >> single matrix explained by multiple matrices. I'm probably missing >> something. If you could explain more I'd be very interested. >> >> Thanks for your help! >> Eliot >> >> >> On Fri, Mar 14, 2014 at 2:16 AM, Paolo Piras <[email protected]>wrote: >> >>> Hi, >>> maybe partial least squares: >>> >>> you can run two separate partial least squares analyses and then >>> comparing vectors. >>> >>> best >>> paolo >>> ________________________________________ >>> Da: [email protected] < >>> [email protected]> per conto di Eliot Miller < >>> [email protected]> >>> Inviato: venerdì 14 marzo 2014 05.50 >>> A: [email protected] >>> Oggetto: [R-sig-eco] Comparing results of two CCAs >>> >>> I have four datasets: morphological measurements for a set of species >>> (M1), >>> ecological measurements for the same set of species (E1), morphological >>> measurements for a second set of species (M2), and ecological >>> measurements >>> for this second set of species (E2). >>> >>> I am interested in finding the linear combinations of variables between >>> M1 >>> and E1, and between M2 and E2. That is, I'd like to know what >>> combinations >>> of morphological measurements are associated with what combination of >>> ecological measurements--for each set of species separately. This seems >>> like a good use of CCA (two separate CCAs). >>> >>> But here's where things get tricky for me. I'd like to see whether the >>> same >>> linear combinations from one set of species do a good job of explaining >>> the >>> variation in the second set of matrices. And I'd like to see how they >>> differ, if possible...e.g. yes the canonical function from the first CCA >>> does explain some of the variation in the second, but a different >>> function >>> could do a lot better. >>> >>> Is this making any sense? I could see simply running these as two >>> separate >>> CCAs, then comparing the results qualitatively. But that doesn't seem >>> very >>> rigorous. Should I be considering some other approach entirely? >>> >>> Thanks for any input! >>> >>> [[alternative HTML version deleted]] >>> >>> _______________________________________________ >>> R-sig-ecology mailing list >>> [email protected] >>> https://stat.ethz.ch/mailman/listinfo/r-sig-ecology >>> >>> >>> >> > [[alternative HTML version deleted]]
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