Selecting specific partial warps does not make sense. You should take the first N PWs, e.g. use the first 5 or 10 PWs but not the first, the third and the tenth. This makes sense if you expect the signal to be large scale. If you do not have this assumption than maybe better use the first few principal components (RWs) instead.
Best, Philipp Mitteroecker Institute for Anthropology University of Vienna [EMAIL PROTECTED] morphmet <[EMAIL PROTECTED]> wrote: > Dear all, > > I have a question concerning the use of partial warp scores in discriminant > analysis (DA)/CVA. I have an outline data, including a couple of true > landmarks and many sliding semilandmarks, on moth genitalia. I use this > data in order to test how well it applies in categorizing study specimens > into correct species (closely resembling each other in genitalia but not in > wing patterns) and I repeat this with several species groups. There are, > however, too many partial warps for the DA since I have only 20-40 > specimens for creating discriminant functions (the rest one-third of > specimens are used for cross-validation). For this reason, I selected only > those partial warp scores showing strong statistical differences between > the species in MANOVA. I got pretty nice results since also those specimens > that were not used in creating discriminant functions were classified > correctly. But is this correct? Can I select those partial warp scores that > differ in MANOVA and use them alone in DA/CVA? I could also reduce the > number of sliding semilandmarks, but since I use outline data with sliding > landmarks, this could possibly reduce the power of DA/CVA? > > I would be grateful for all advices, > > Marko Mutanen > University of Oulu > Finland > [EMAIL PROTECTED] > -- > Replies will be sent to the list. > For more information visit http://www.morphometrics.org -- Replies will be sent to the list. For more information visit http://www.morphometrics.org
