On Sun, 2010-06-27 at 17:56 -0300, Alexandre F. Souza wrote: > Hi all, > > I am using vegan to run a pca on forest structural variables (tree > density, basal area, average height, regeneration density) in R. > However, I could not find out how to extract factor loadings > (correlations of each variable with each pca axis).
Loadings on each axis are given by: scores(ord, display = "species", scaling = 2) where 'ord' is your fitted ordination. I this scaling, angles between the vectors defined by the scores on axis 1 and 2 are correlations. Loadings aren't correlations themselves, however, they define a vector in space and you need to compute the angle between this vector and each axis. I forget exactly how to compute the correlation between the vector representing the species in n dimensions (n = 2) and each axis; sounds like some elementary trigonometry is required (estimate angles of a triangle with sides of lengths given by axis 1 score [ax1], axis 2 score [ax2] and sqrt(ax1^2 + ax2^2) i.e. from Pythagorean distance), but the scores give you everything you need to do this except for the trig. If you have further questions, post on R-SIG-Ecology list or the Vegan help forums: https://r-forge.r-project.org/forum/forum.php?forum_id=194&group_id=68 HTH G > > Do anyone know how to do that? > > Thanks a lot, > > Alexandre > > > > Dr. Alexandre F. Souza > Programa de Pós-Graduação em Biologia: Diversidade e Manejo da Vida > Silvestre > Universidade do Vale do Rio dos Sinos (UNISINOS) > Av. UNISINOS 950 - C.P. 275, São Leopoldo 93022-000, RS - Brasil > Telefone: (051)3590-8477 ramal 1263 > Skype: alexfadigas > [email protected] > http://www.unisinos.br/laboratorios/lecopop > -- %~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~% Dr. Gavin Simpson [t] +44 (0)20 7679 0522 ECRC, UCL Geography, [f] +44 (0)20 7679 0565 Pearson Building, [e] gavin.simpsonATNOSPAMucl.ac.uk Gower Street, London [w] http://www.ucl.ac.uk/~ucfagls/ UK. WC1E 6BT. [w] http://www.freshwaters.org.uk %~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%
