Dear Oliver, Yes, you can use the independent contrasts to estimate the correlations (e.g., assuming BM). Otherwise, you can compute the correlations directly from the models fit in mvMORPH. For instance, under BM you can use: fit_bm <- mvBM(tree, data) cov2cor(fit_bm$sigma) # marginal correlations cor2pcor(fit_bm$sigma) # partial correlations Under OU, you can also retrieve the correlations from the variance-covariance matrix (which depends on both the “sigma” and “alpha” parameters, and can be retrieved with the “stationary” function): fit_ou <- mvOU(tree, data) cov2cor(stationary(fit_ou$sigma)) # marginal correlations cor2pcor(stationary(fit_ou)) # partial correlations Alternatively, you can use the “mvgls” function to do it, e.g.:
fit_bm2 <- mvgls(data~1, tree=tree, model="BM", method="LL") cov2cor(fit_bm2$sigma$Pinv) # marginal correlations cor2pcor(fit_bm2$sigma$Pinv) # partial correlations You can for instance use penalized likelihood to obtain a regularized estimate of the evolutionary correlations: fit_bm2 <- mvgls(data~1, tree=tree, model="BM", method="PL") # Ridge penalization by default cov2cor(fit_bm2$sigma$Pinv) # marginal correlations cor2pcor(fit_bm2$sigma$Pinv) # partial correlations With LASSO penalization, for instance, you can find a sparse estimate for the partial correlations. That is, you can directly select the “significant” partial correlations from the model fit: fit_bm2 <- mvgls(data~1, tree=tree, model="BM", method="PL", penalty="LASSO") cov2cor(fit_bm2$sigma$Pinv) # marginal correlations cor2pcor(fit_bm2$sigma$Pinv) # partial correlations Best wishes, Julien De : R-sig-phylo <r-sig-phylo-boun...@r-project.org> de la part de Oliver Betz <oliver.b...@uni-tuebingen.de> Envoyé : dimanche 13 juin 2021 14:45 À : r-sig-phylo@r-project.org <r-sig-phylo@r-project.org> Objet : [R-sig-phylo] phylogenetic correlation analysis Dear all: I would like to perform a phylogenetic correlation analysis (simlar to PGLS, but correlation instead of regression), so that I get a correlation matrix, where all the Pearson or Spearman correlation coefficients between all of my variables are listed. One solution might be to calculate PICs and do standard correlation analyses on them, but there might be a more direct solution available? Which R package would you recommend for such analysis? Thank you very much, Oliver Betz _______________________________________________ R-sig-phylo mailing list - R-sig-phylo@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-phylo Searchable archive at http://www.mail-archive.com/r-sig-phylo@r-project.org/ _______________________________________________ R-sig-phylo mailing list - R-sig-phylo@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-phylo Searchable archive at http://www.mail-archive.com/r-sig-phylo@r-project.org/
