library("phylolm") source("R2_Supplement_Appendix_S1_source_code.R") tree <- read.tree("tree") data<- read.table("data.csv", header=TRUE) attach(data) data$AI <- as.factor(data$Arid) attach(data) names(data) rownames(data) <- data[,1] attach(data) head(data) DF.data <- data[tree$tip.label, ] DF.data attach(DF.data) head(DF.data) ######### DF.data$lBT <- scale(log10(Tmin),center = TRUE,scale = TRUE) DF.data$lBM <- scale(log10(BM),center = TRUE,scale = TRUE) DF.data$lBio1 <- scale(log10(Bio1Med),center = TRUE,scale = TRUE) DF.data$lBio1SD <- scale(log10(Bio1SD),center = TRUE,scale = TRUE) DF.data$lBio2 <- scale(log10(Bio2Med),center = TRUE,scale = TRUE) DF.data$lBio4 <- scale(log10(Bio4Med),center = TRUE,scale = TRUE) DF.data$lAYM <- scale(log10(AYM_Med),center = TRUE,scale = TRUE) DF.data$lAYSD <- scale(log10(AYSD_Med),center = TRUE,scale = TRUE) DF.data$lASDYM <- scale(log10(ASDYM_Med),center = TRUE,scale = TRUE) attach(DF.data) library(psych) dataPCA<-DF.data[c(26:32)] dataPCA Rep_matrix<-dataPCA[,1:7] Rep_matrix<-as.data.frame(Rep_matrix) PCA_ext<-principal(Rep_matrix, nfactors = 5, scores=TRUE, rotate = "varimax") PCA_ext summary(PCA_ext) PCA_scores_ext<-as.data.frame(PCA_ext$scores) PCA_scores_ext dataPCA<- cbind(DF.data,PCA_scores_ext) dataPCA fit1 <- phylolm(lBT ~ AI + lBM + RC1 + RC2 + RC3 + RC4 + RC5 + AI*lBM + AI*RC1 + AI*RC2 + AI*RC3 + AI*RC4 + AI*RC5 + lBM*RC1 + lBM*RC2 + lBM*RC3 + lBM*RC4 + lBM*RC5, data=dataPCA, phy=tree, model="lambda") summary(fit1) fit1 <- phylolm(lBT ~ AI + lBM + RC1 + RC2 + RC3 + RC4 + RC5 + AI*RC4, data=dataPCA, phy=tree, model="lambda") summary(fit1) R2.ls(fit1, phy=tree) R2.ce(fit1, phy=tree) R2.lr(fit1) fit1 <- phylolm(lBT ~ AI + RC1 + RC2 + RC4 + RC5 + AI*RC4, data=dataPCA, phy=tree, model="lambda") summary(fit1) R2.ls(fit1, phy=tree) R2.ce(fit1, phy=tree) R2.lr(fit1)