Hi Lior, R-squared and Pearson's coefficient are two different things:
R-squared (aka coefficient of determination) quantifies the quantity of variance of a variable (the response, 'y' in your example) that is explained by the model where some predictors can be controlled by the experimenter. This concept is difficult to apply in evolutionary models since variables are (usually) not controlled. The correlation coefficient quantifies the link between two uncontrolled variables without any assumption whether one is a response and the other is the predictor. In the case of phylogenetic comparative analyses, the correlation between PICs is certainly what you are looking for: see ?pic in ape. HTH, Best, Emmanuel ----- Le 18 Juil 23, à 19:29, Lior Glick liorg...@mail.tau.ac.il a écrit : > Hello, > > I ran a PGLS analysis like this: > model = gls(y ~ x, correlation = corBrownian(phy = tree, form=~species), > data = data_df, method = "ML") > > I was able to extract the coefficients (intercept + b1 in this case), as > well as the relevant p-values. What I couldn't figure out is how to get a > correlation coefficient between y and x, something like R-squared or > pearson's coefficient. > > Can you please help with that? > Thanks! > > [[alternative HTML version deleted]] > > _______________________________________________ > 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/
