R code for PGLS analysis and brain transformations 

# Regressions of brain volumes 

# Calculating relative brain size

library("ape")
library("geiger")
library("caper")
carnrbs <- read.csv("/Users/helen/OneDrive/Documents/RBSCarn.csv")
mytree <- read.nexus("/Users/helen/OneDrive/Documents/Uphametal2019tree")
myframe <- data.frame(carnrbs)
rownames(myframe) <- carnrbs$S
name.check(mytree, myframe, data.names = myframe$S)
matches<-match(mytree$tip.label, carnrbs$S, nomatch=0)
not<-subset(mytree$tip.label, matches==0)
tree_pruned<-drop.tip(mytree, not)
mycombi <- comparative.data(phy = tree_pruned, data = myframe, names.col = S, vcv = TRUE, na.omit = FALSE, warn.dropped = TRUE)
carn <- pgls(LogECV ~ LogMass, data = mycombi, lambda = "ML")
residuals(carn)

# Calculating relative neocortex size 

library("ape")
library("geiger")
library("caper")
carnneo <- read.csv("/Users/helen/OneDrive/Documents/CarnNeo.csv")
mytree <- read.nexus("/Users/helen/OneDrive/Documents/Uphametal2019tree")
myframe <- data.frame(carnneo)
rownames(myframe) <- carnneo$S
name.check(mytree, myframe, data.names = myframe$S)
matches<-match(mytree$tip.label, carnneo$S, nomatch=0)
not<-subset(mytree$tip.label, matches==0)
tree_pruned<-drop.tip(mytree, not)
mycombi <- comparative.data(phy = tree_pruned, data = myframe, names.col = S, vcv = TRUE, na.omit = FALSE, warn.dropped = TRUE)
carnneo <- pgls(LogNeo ~ LogMass, data = mycombi, lambda = "ML")
residuals(carnneo)

# Calculating relative cerebellum size 

library("ape")
library("geiger")
library("caper")
carncere <- read.csv("/Users/helen/OneDrive/Documents/CarnCere.csv")
mytree <- read.nexus("/Users/helen/OneDrive/Documents/Uphametal2019tree")
myframe <- data.frame(carncere)
rownames(myframe) <- carncere$S
name.check(mytree, myframe, data.names = myframe$S)
matches<-match(mytree$tip.label, carncere$S, nomatch=0)
not<-subset(mytree$tip.label, matches==0)
tree_pruned<-drop.tip(mytree, not)
mycombi <- comparative.data(phy = tree_pruned, data = myframe, names.col = S, vcv = TRUE, na.omit = FALSE, warn.dropped = TRUE)
carncere <- pgls(LogCere ~ LogM, data = mycombi, lambda = "ML")
residuals(carncere)

# Calculating EQ

eq <- read.csv("/Users/helen/OneDrive/Documents/PrimRBS.csv")
model.lm <- lm(LogECV ~ LogMass, data = eq)
m = 10^(model.lm$coefficients) ## intercept
e = model.lm$coefficients ##slope

# Repeat all steps again using primate data

# PGLS Models Primates

# ECV Models 

# Social 
 
library("ape")
library("geiger")
library("caper")
model1p <- read.csv("/Users/helen/OneDrive/Documents/Model1P.csv")
myframe <- data.frame(model1p)
rownames(myframe) <- model1p$S
mytree <- read.nexus("/Users/helen/OneDrive/Documents/Uphametal2019tree")
matches <- match(mytree$tip.label, model1p$S, nomatch = 0)
not<-subset(mytree$tip.label, matches==0)
tree_pruned<-drop.tip(mytree, not)
mycombi <- comparative.data(phy = tree_pruned, data = myframe, names.col = S, vcv = TRUE, na.omit = FALSE, warn.dropped = TRUE)
model1 <- pgls(LogECV ~ LogMass + LogGS + SC, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + LogGS, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + SC, data = mycombi, lambda = "ML" )
BIC(model1)

#Ecological 

library("ape")
library("geiger")
library("caper")
model1p <- read.csv("/Users/helen/OneDrive/Documents/Model1P.csv")
model1p$D <- as.factor(model1p$D)
myframe <- data.frame(model1p)
rownames(myframe) <- model1p$S
mytree <- read.nexus("/Users/helen/OneDrive/Documents/Uphametal2019tree")
matches <- match(mytree$tip.label, model1p$S, nomatch = 0)
not<-subset(mytree$tip.label, matches==0)
tree_pruned<-drop.tip(mytree, not)
mycombi <- comparative.data(phy = tree_pruned, data = myframe, names.col = S, vcv = TRUE, na.omit = FALSE, warn.dropped = TRUE)
model1 <- pgls(LogECV ~ LogMass + HV, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + HV + LogHR, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + LogHR, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + D + HV + LogHR, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + D + HV, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + D + LogHR, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + D, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + D + DB + HV + LogHR, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + D + DB + HV, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + DB + HV, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + DB + HV + LogHR, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + D + DB + LogHR, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + D + DB, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + DB, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + DB + LogHR, data = mycombi, lambda = "ML" )
BIC(model1) 

# Social & Ecological 

library("ape")
library("geiger")
library("caper")
model1p <- read.csv("/Users/helen/OneDrive/Documents/Model1P.csv")
model1p$D <- as.factor(model1p$D)
myframe <- data.frame(model1p)
rownames(myframe) <- model1p$S
mytree <- read.nexus("/Users/helen/OneDrive/Documents/Uphametal2019tree")
matches <- match(mytree$tip.label, model1p$S, nomatch = 0)
not<-subset(mytree$tip.label, matches==0)
tree_pruned<-drop.tip(mytree, not)
mycombi <- comparative.data(phy = tree_pruned, data = myframe, names.col = S, vcv = TRUE, na.omit = FALSE, warn.dropped = TRUE)
model1 <- pgls(LogECV ~ LogMass + LogGS + SC + HV + LogHR, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + LogGS + HV + LogHR, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + LogGS + SC + HV, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + SC + HV + LogHR, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + LogGS + HV, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + LogGS + SC + LogHR, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + SC + HV, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + LogGS + LogHR, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + LogGS + D + HV + LogHR, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + LogGS + SC + D + HV + LogHR, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + SC + LogHR, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + LogGS + SC + D + HV, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + LogGS + D + HV, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + LogGS + D + LogHR, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + LogGS + SC + D + LogHR, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + SC + D + HV + LogHR, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + LogGS + SC + D, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + SC + D + HV, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + LogGS + D, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + SC + D + LogHR, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + LogGS + D + DB + HV + LogHR, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + SC + D, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + LogGS + D + DB + HV, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + LogGS + D + DB + LogHR, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + LogGS + DB + HV + LogHR, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + LogGS + SC + D + DB + HV + LogHR, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + LogGS + D + DB, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + LogGS + DB + HV, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + LogGS + SC + D + DB + HV, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + LogGS + SC + D + DB + LogHR, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + LogGS + SC + D + DB, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + LogGS + DB + LogHR, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + LogGS + SC + DB + HV, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + LogGS + SC + DB + HV + LogHR, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + SC + D + DB + HV + LogHR, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + LogGS + DB, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + LogGS + SC + DB, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + SC + D + DB + LogHR, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + SC + D + DB + HV, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + LogGS + SC + DB + LogHR, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + SC + D + DB, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + SC + DB + HV, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + SC + DB + HV + LogHR, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + SC + DB, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + SC + DB + LogHR, data = mycombi, lambda = "ML" )
BIC(model1) 

# Life History 

library("ape")
library("geiger")
library("caper")
model1p <- read.csv("/Users/helen/OneDrive/Documents/Model1P.csv")
myframe <- data.frame(model1p)
rownames(myframe) <- model1p$S
mytree <- read.nexus("/Users/helen/OneDrive/Documents/Uphametal2019tree")
matches <- match(mytree$tip.label, model1p$S, nomatch = 0)
not<-subset(mytree$tip.label, matches==0)
tree_pruned<-drop.tip(mytree, not)
mycombi <- comparative.data(phy = tree_pruned, data = myframe, names.col = S, vcv = TRUE, na.omit = FALSE, warn.dropped = TRUE)
model1 <- pgls(LogECV ~ LogMass + LogF + LogFR, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + LogFR, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(LogECV ~ LogMass + LogF, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(LogECV ~ LogMass + LogML + LogFR, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(LogECV ~ LogMass + LogML + LogF + LogFR, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(LogECV ~ LogMass + LogML, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(LogECV ~ LogMass + LogML + LogF, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(LogECV ~ LogMass + LogGL + LogF + LogFR, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(LogECV ~ LogMass + LogF + LogFR + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(LogECV ~ LogMass + LogGL + LogFR, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(LogECV ~ LogMass + LogGL + LogF, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(LogECV ~ LogMass + LogGL, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(LogECV ~ LogMass + LogFR + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(LogECV ~ LogMass + LogF + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(LogECV ~ LogMass + LogGL + LogML + LogF + LogFR, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(LogECV ~ LogMass + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(LogECV ~ LogMass + LogGL + LogML + LogFR, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(LogECV ~ LogMass + LogGL + LogML + LogF, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(LogECV ~ LogMass + LogGL + LogF + LogFR + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(LogECV ~ LogMass + LogGL + LogML, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(LogECV ~ LogMass + LogGL + LogF + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(LogECV ~ LogMass + LogGL + LogFR + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(LogECV ~ LogMass + LogML + LogF + LogFR + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(LogECV ~ LogMass + LogGL + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(LogECV ~ LogMass + LogML + LogFR + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(LogECV ~ LogMass + LogML + LogF + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(LogECV ~ LogMass + LogML + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(LogECV ~ LogMass + LogGL + LogML + LogF + LogFR + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(LogECV ~ LogMass + LogGL + LogML + LogFR + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(LogECV ~ LogMass + LogGL + LogML + LogF + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(LogECV ~ LogMass + LogGL + LogML + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)

# Combined 
library("ape")
library("geiger")
library("caper")
model1p <- read.csv("/Users/helen/OneDrive/Documents/Model1P.csv")
model1p$D <- as.factor(model1p$D)
myframe <- data.frame(model1p)
rownames(myframe) <- model1p$S
mytree <- read.nexus("/Users/helen/OneDrive/Documents/Uphametal2019tree")
matches <- match(mytree$tip.label, model1p$S, nomatch = 0)
not<-subset(mytree$tip.label, matches==0)
tree_pruned<-drop.tip(mytree, not)
mycombi <- comparative.data(phy = tree_pruned, data = myframe, names.col = S, vcv = TRUE, na.omit = FALSE, warn.dropped = TRUE)
model1 <- pgls(LogECV ~ LogMass + SC + DB + LogHR + LogML + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(LogECV ~ LogMass + SC + DB + LogML + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(LogECV ~ LogMass + DB + LogML + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(LogECV ~ LogMass + SC + DB + LogHR + LogGL + LogML + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(LogECV ~ LogMass + LogGS + SC + D + DB + HV + LogHR + LogGL + LogML + LogF + LogFR + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(LogECV ~ LogMass + DB + LogHR + LogGL + LogML + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(LogECV ~ LogMass + DB + LogGL + LogML + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(LogECV ~ LogMass + SC + DB + LogGL + LogML + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(LogECV ~ LogMass + LogGS + DB + LogGL + LogML + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)

# RBS Models 

# Repeat social, ecological, social & ecological, and life history models as outlined above 

# Combined
library("ape")
library("geiger")
library("caper")
model2p <- read.csv("/Users/helen/OneDrive/Documents/Model2P.csv")
model2p$D <- as.factor(model2p$D)
myframe <- data.frame(model2p)
rownames(myframe) <- model2p$S
mytree <- read.nexus("/Users/helen/OneDrive/Documents/Uphametal2019tree")
matches <- match(mytree$tip.label, model2p$S, nomatch = 0)
not<-subset(mytree$tip.label, matches==0)
tree_pruned<-drop.tip(mytree, not)
mycombi <- comparative.data(phy = tree_pruned, data = myframe, names.col = S, vcv = TRUE, na.omit = FALSE, warn.dropped = TRUE)
model1 <- pgls(RBS ~ LogGS + SC + D + DB + HV + LogHR + LogGL + LogML + LogF + LogFR + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(RBS ~ SC + DB + LogGL, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(RBS ~ LogGS + DB + LogGL, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(RBS ~ LogGS + DB + LogGL + LogML, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(RBS ~ SC + DB + LogGL + LogML, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(RBS ~ SC + DB + LogGL + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(RBS ~ SC + DB + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(RBS ~ LogGS + DB + LogML + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(RBS ~ LogGS + DB + LogGL + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(RBS ~ SC + DB + LogML + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(RBS ~ LogGS + DB + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(RBS ~ LogGS + DB + LogGL + LogML + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(RBS ~ SC + DB + LogGL + LogML + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)

# EQ Models 

# Repeat social, ecological, social & ecological, and life history models as outlined above 

# Combined
library("ape")
library("geiger")
library("caper")
model3p <- read.csv("/Users/helen/OneDrive/Documents/Model3P.csv")
model3p$D <- as.factor(model3p$D)
myframe <- data.frame(model3p)
rownames(myframe) <- model3p$S
mytree <- read.nexus("/Users/helen/OneDrive/Documents/Uphametal2019tree")
matches <- match(mytree$tip.label, model3p$S, nomatch = 0)
not<-subset(mytree$tip.label, matches==0)
tree_pruned<-drop.tip(mytree, not)
mycombi <- comparative.data(phy = tree_pruned, data = myframe, names.col = S, vcv = TRUE, na.omit = FALSE, warn.dropped = TRUE)
model1 <- pgls(EQ ~ LogGS + SC + D + DB + HV + LogHR + LogGL + LogML + LogF + LogFR + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(EQ ~ DB + LogF, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(EQ ~ DB + LogGL, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(EQ ~ DB + LogML, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(EQ ~ DB + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(EQ ~ DB + LogFR, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(EQ ~ SC + DB + LogF, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(EQ ~ LogGS + DB + LogF, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(EQ ~ LogGS + DB + LogGL, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(EQ ~ LogGS + DB + LogML, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(EQ ~ LogGS + DB + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(EQ ~ LogGS + DB + LogFR, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(EQ ~ SC + DB + LogGL, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(EQ ~ SC + DB + LogML, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(EQ ~ SC + DB + LogFR, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(EQ ~ SC + DB + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)

# RNS Models 

# Repeat social, ecological, social & ecological, and life history models as outlined above 

# Combined
library("ape")
library("geiger")
library("caper")
model4p <- read.csv("/Users/helen/OneDrive/Documents/Model4P.csv")
model4p$D <- as.factor(model4p$D)
myframe <- data.frame(model4p)
rownames(myframe) <- model4p$S
mytree <- read.nexus("/Users/helen/OneDrive/Documents/Uphametal2019tree")
matches <- match(mytree$tip.label, model4p$S, nomatch = 0)
not<-subset(mytree$tip.label, matches==0)
tree_pruned<-drop.tip(mytree, not)
mycombi <- comparative.data(phy = tree_pruned, data = myframe, names.col = S, vcv = TRUE, na.omit = FALSE, warn.dropped = TRUE)
model1 <- pgls(RNS ~ LogGS + SC + D + DB + HV + LogHR + LogGL + LogML + LogF + LogFR + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(RNS ~ D + LogGL, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(RNS ~ D + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(RNS ~ D + LogF, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(RNS ~ D + LogHR + LogGL, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(RNS ~ D + LogHR + LogF, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(RNS ~ D + LogHR + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(RNS ~ D + LogML + LogF, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(RNS ~ DB + LogHR + LogGL, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(RNS ~ D + LogHR + LogML + LogF, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(RNS ~ D + LogML, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(RNS ~ DB + LogHR + LogF, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(RNS ~ DB + LogHR + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(RNS ~ D + LogHR + LogML, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(RNS ~ DB + LogHR + LogML, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(RNS ~ DB + LogHR + LogML + LogF, data = mycombi, lambda = "ML" )
BIC(model1)

# RCS Models 

# Repeat social, ecological, social & ecological, and life history models as outlined above 

# Combined
library("ape")
library("geiger")
library("caper")
model5p <- read.csv("/Users/helen/OneDrive/Documents/Model5P.csv")
model5p$D <- as.factor(model5p$D)
myframe <- data.frame(model5p)
rownames(myframe) <- model5p$S
mytree <- read.nexus("/Users/helen/OneDrive/Documents/Uphametal2019tree")
matches <- match(mytree$tip.label, model5p$S, nomatch = 0)
not<-subset(mytree$tip.label, matches==0)
tree_pruned<-drop.tip(mytree, not)
mycombi <- comparative.data(phy = tree_pruned, data = myframe, names.col = S, vcv = TRUE, na.omit = FALSE, warn.dropped = TRUE)
model1 <- pgls(RCS ~ LogGS + SC + D + DB + HV + LogHR + LogGL + LogML + LogF + LogFR + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(RCS ~ SC + D + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(RCS ~ SC + D + LogF, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(RCS ~ D + LogHR + LogF, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(RCS ~ D + LogHR + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(RCS ~ D + LogHR + LogFR, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(RCS ~ SC + D + LogHR + LogF, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(RCS ~ SC + D + LogHR + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(RCS ~ D + DB + LogHR + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(RCS ~ D + DB + LogHR + LogF, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(RCS ~ D + LogHR + LogGL, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(RCS ~ SC + D + LogGL, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(RCS ~ LogGS + D + LogHR + LogF, data = mycombi, lambda = "ML" )
BIC(model1)

# Neo Models 

# Repeat social, ecological, social & ecological, and life history models as outlined above 

# Combined
library("ape")
library("geiger")
library("caper")
model6p <- read.csv("/Users/helen/OneDrive/Documents/Model6P.csv")
model6p$D <- as.factor(model6p$D)
myframe <- data.frame(model6p)
rownames(myframe) <- model6p$S
mytree <- read.nexus("/Users/helen/OneDrive/Documents/Uphametal2019tree")
matches <- match(mytree$tip.label, model6p$S, nomatch = 0)
not<-subset(mytree$tip.label, matches==0)
tree_pruned<-drop.tip(mytree, not)
mycombi <- comparative.data(phy = tree_pruned, data = myframe, names.col = S, vcv = TRUE, na.omit = FALSE, warn.dropped = TRUE)
model1 <- pgls(LogNeoROB ~ LogGS + SC + D + DB + HV + LogHR + LogGL + LogML + LogF + LogFR + LogWA, data = mycombi, lambda = "ML")
BIC(model1)
model1 <- pgls(LogNeoROB ~ D + LogHR + LogML + LogWA, data = mycombi, lambda = "ML")
BIC(model1)
model1 <- pgls(LogNeoROB ~ SC + D + LogHR + LogML + LogWA, data = mycombi, lambda = "ML")
BIC(model1)
model1 <- pgls(LogNeoROB ~ LogGS + D + LogHR + LogML + LogWA, data = mycombi, lambda = "ML")
BIC(model1)

# Cere Models 

# Repeat social, ecological, social & ecological, and life history models as outlined above 

# Combined
library("ape")
library("geiger")
library("caper")
model7p <- read.csv("/Users/helen/OneDrive/Documents/Model7P.csv")
model7p$D <- as.factor(model7p$D)
myframe <- data.frame(model7p)
rownames(myframe) <- model7p$S
mytree <- read.nexus("/Users/helen/OneDrive/Documents/Uphametal2019tree")
matches <- match(mytree$tip.label, model7p$S, nomatch = 0)
not<-subset(mytree$tip.label, matches==0)
tree_pruned<-drop.tip(mytree, not)
mycombi <- comparative.data(phy = tree_pruned, data = myframe, names.col = S, vcv = TRUE, na.omit = FALSE, warn.dropped = TRUE)
model1 <- pgls(LogCereROB ~ LogGS + SC + D + DB + HV + LogHR + LogGL + LogML + LogF + LogFR + LogWA, data = mycombi, lambda = "ML")
BIC(model1)
model1 <- pgls(LogCereROB ~ D + LogHR + LogML + LogWA, data = mycombi, lambda = "ML")
BIC(model1)
model1 <- pgls(LogCereROB ~ D + LogHR + LogGL + LogML + LogWA, data = mycombi, lambda = "ML")
BIC(model1)
model1 <- pgls(LogCereROB ~ LogGS + D + LogHR + LogGL + LogML + LogWA, data = mycombi, lambda = "ML")
BIC(model1)
model1 <- pgls(LogCereROB ~ LogGS + D + LogHR + LogML + LogWA, data = mycombi, lambda = "ML")
BIC(model1)
model1 <- pgls(LogCereROB ~ SC + D + LogHR + LogML + LogWA, data = mycombi, lambda = "ML")
BIC(model1)
model1 <- pgls(LogCereROB ~ SC + D + LogHR + LogGL + LogML + LogWA, data = mycombi, lambda = "ML")
BIC(model1)

# PGLS Models Carnivores

# ECV Models 

# Social 

library("ape")
library("geiger")
library("caper")
model1c <- read.csv("/Users/helen/OneDrive/Documents/Model1C.csv")
myframe <- data.frame(model1c)
rownames(myframe) <- model1c$S
mytree <- read.nexus("/Users/helen/OneDrive/Documents/Uphametal2019tree")
matches <- match(mytree$tip.label, model1c$S, nomatch = 0)
not<-subset(mytree$tip.label, matches==0)
tree_pruned<-drop.tip(mytree, not)
mycombi <- comparative.data(phy = tree_pruned, data = myframe, names.col = S, vcv = TRUE, na.omit = FALSE, warn.dropped = TRUE)
model1 <- pgls(LogECV ~ LogMass + LogGS + SC, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + LogGS, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + SC, data = mycombi, lambda = "ML" )
BIC(model1)

#Ecological 

library("ape")
library("geiger")
library("caper")
model1c <- read.csv("/Users/helen/OneDrive/Documents/Model1C.csv")
model1c$D <- as.factor(model1c$D)
myframe <- data.frame(model1c)
rownames(myframe) <- model1c$S
mytree <- read.nexus("/Users/helen/OneDrive/Documents/Uphametal2019tree")
matches <- match(mytree$tip.label, model1c$S, nomatch = 0)
not<-subset(mytree$tip.label, matches==0)
tree_pruned<-drop.tip(mytree, not)
mycombi <- comparative.data(phy = tree_pruned, data = myframe, names.col = S, vcv = TRUE, na.omit = FALSE, warn.dropped = TRUE)
model1 <- pgls(LogECV ~ LogMass + HV, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + HV + LogHR, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + LogHR, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + D + HV + LogHR, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + D + HV, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + D + LogHR, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + D, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + D + DB + HV + LogHR, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + D + DB + HV, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + DB + HV, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + DB + HV + LogHR, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + D + DB + LogHR, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + D + DB, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + DB, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + DB + LogHR, data = mycombi, lambda = "ML" )
BIC(model1) 

# Social & Ecological 

library("ape")
library("geiger")
library("caper")
model1c <- read.csv("/Users/helen/OneDrive/Documents/Model1C.csv")
model1c$D <- as.factor(model1c$D)
myframe <- data.frame(model1c)
rownames(myframe) <- model1c$S
mytree <- read.nexus("/Users/helen/OneDrive/Documents/Uphametal2019tree")
matches <- match(mytree$tip.label, model1c$S, nomatch = 0)
not<-subset(mytree$tip.label, matches==0)
tree_pruned<-drop.tip(mytree, not)
mycombi <- comparative.data(phy = tree_pruned, data = myframe, names.col = S, vcv = TRUE, na.omit = FALSE, warn.dropped = TRUE)
model1 <- pgls(LogECV ~ LogMass + LogGS + SC + HV + LogHR, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + LogGS + HV + LogHR, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + LogGS + SC + HV, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + SC + HV + LogHR, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + LogGS + HV, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + LogGS + SC + LogHR, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + SC + HV, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + LogGS + LogHR, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + LogGS + D + HV + LogHR, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + LogGS + SC + D + HV + LogHR, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + SC + LogHR, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + LogGS + SC + D + HV, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + LogGS + D + HV, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + LogGS + D + LogHR, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + LogGS + SC + D + LogHR, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + SC + D + HV + LogHR, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + LogGS + SC + D, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + SC + D + HV, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + LogGS + D, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + SC + D + LogHR, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + LogGS + D + DB + HV + LogHR, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + SC + D, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + LogGS + D + DB + HV, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + LogGS + D + DB + LogHR, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + LogGS + DB + HV + LogHR, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + LogGS + SC + D + DB + HV + LogHR, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + LogGS + D + DB, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + LogGS + DB + HV, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + LogGS + SC + D + DB + HV, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + LogGS + SC + D + DB + LogHR, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + LogGS + SC + D + DB, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + LogGS + DB + LogHR, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + LogGS + SC + DB + HV, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + LogGS + SC + DB + HV + LogHR, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + SC + D + DB + HV + LogHR, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + LogGS + DB, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + LogGS + SC + DB, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + SC + D + DB + LogHR, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + SC + D + DB + HV, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + LogGS + SC + DB + LogHR, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + SC + D + DB, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + SC + DB + HV, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + SC + DB + HV + LogHR, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + SC + DB, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + SC + DB + LogHR, data = mycombi, lambda = "ML" )
BIC(model1) 

# Life History 

library("ape")
library("geiger")
library("caper")
model1c <- read.csv("/Users/helen/OneDrive/Documents/Model1C.csv")
myframe <- data.frame(model1c)
rownames(myframe) <- model1c$S
mytree <- read.nexus("/Users/helen/OneDrive/Documents/Uphametal2019tree")
matches <- match(mytree$tip.label, model1c$S, nomatch = 0)
not<-subset(mytree$tip.label, matches==0)
tree_pruned<-drop.tip(mytree, not)
mycombi <- comparative.data(phy = tree_pruned, data = myframe, names.col = S, vcv = TRUE, na.omit = FALSE, warn.dropped = TRUE)
model1 <- pgls(LogECV ~ LogMass + LogF + LogFR, data = mycombi, lambda = "ML" )
BIC(model1) 
model1 <- pgls(LogECV ~ LogMass + LogFR, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(LogECV ~ LogMass + LogF, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(LogECV ~ LogMass + LogML + LogFR, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(LogECV ~ LogMass + LogML + LogF + LogFR, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(LogECV ~ LogMass + LogML, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(LogECV ~ LogMass + LogML + LogF, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(LogECV ~ LogMass + LogGL + LogF + LogFR, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(LogECV ~ LogMass + LogF + LogFR + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(LogECV ~ LogMass + LogGL + LogFR, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(LogECV ~ LogMass + LogGL + LogF, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(LogECV ~ LogMass + LogGL, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(LogECV ~ LogMass + LogFR + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(LogECV ~ LogMass + LogF + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(LogECV ~ LogMass + LogGL + LogML + LogF + LogFR, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(LogECV ~ LogMass + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(LogECV ~ LogMass + LogGL + LogML + LogFR, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(LogECV ~ LogMass + LogGL + LogML + LogF, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(LogECV ~ LogMass + LogGL + LogF + LogFR + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(LogECV ~ LogMass + LogGL + LogML, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(LogECV ~ LogMass + LogGL + LogF + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(LogECV ~ LogMass + LogGL + LogFR + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(LogECV ~ LogMass + LogML + LogF + LogFR + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(LogECV ~ LogMass + LogGL + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(LogECV ~ LogMass + LogML + LogFR + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(LogECV ~ LogMass + LogML + LogF + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(LogECV ~ LogMass + LogML + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(LogECV ~ LogMass + LogGL + LogML + LogF + LogFR + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(LogECV ~ LogMass + LogGL + LogML + LogFR + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(LogECV ~ LogMass + LogGL + LogML + LogF + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(LogECV ~ LogMass + LogGL + LogML + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)

# Combined 
library("ape")
library("geiger")
library("caper")
model1c <- read.csv("/Users/helen/OneDrive/Documents/Model1C.csv")
model1c$D <- as.factor(model1c$D)
myframe <- data.frame(model1c)
rownames(myframe) <- model1c$S
mytree <- read.nexus("/Users/helen/OneDrive/Documents/Uphametal2019tree")
matches <- match(mytree$tip.label, model1c$S, nomatch = 0)
not<-subset(mytree$tip.label, matches==0)
tree_pruned<-drop.tip(mytree, not)
mycombi <- comparative.data(phy = tree_pruned, data = myframe, names.col = S, vcv = TRUE, na.omit = FALSE, warn.dropped = TRUE)
model1 <- pgls(LogECV ~ LogMass + LogGS + SC + D + DB + HV + LogHR + LogGL + LogML + LogF + LogFR + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(LogECV ~ LogMass + DB + LogF, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(LogECV ~ LogMass + HV + LogF, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(LogECV ~ LogMass + LogGS + LogF, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(LogECV ~ LogMass + SC + LogF, data = mycombi, lambda = "ML" )
BIC(model1)


# RBS Models 

# Repeat social, ecological, social & ecological, and life history models as outlined above 

# Combined
library("ape")
library("geiger")
library("caper")
model2c <- read.csv("/Users/helen/OneDrive/Documents/Model2C.csv")
model2c$D <- as.factor(model2c$D)
myframe <- data.frame(model2c)
rownames(myframe) <- model2c$S
mytree <- read.nexus("/Users/helen/OneDrive/Documents/Uphametal2019tree")
matches <- match(mytree$tip.label, model2c$S, nomatch = 0)
not<-subset(mytree$tip.label, matches==0)
tree_pruned<-drop.tip(mytree, not)
mycombi <- comparative.data(phy = tree_pruned, data = myframe, names.col = S, vcv = TRUE, na.omit = FALSE, warn.dropped = TRUE)
model1 <- pgls(RBS ~ LogGS + SC + D + DB + HV + LogHR + LogGL + LogML + LogF + LogFR + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(RBS ~ DB + LogF, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(RBS ~ HV + LogF, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(RBS ~ DB + LogF + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(RBS ~ DB + LogML, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(RBS ~ HV + LogML, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(RBS ~ DB + LogFR, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(RBS ~ HV + LogFR, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(RBS ~ HV + LogF + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)


# EQ Models 

# Repeat social, ecological, social & ecological, and life history models as outlined above 

# Combined
library("ape")
library("geiger")
library("caper")
model3c <- read.csv("/Users/helen/OneDrive/Documents/Model3C.csv")
model3c$D <- as.factor(model3c$D)
myframe <- data.frame(model3c)
rownames(myframe) <- model3c$S
mytree <- read.nexus("/Users/helen/OneDrive/Documents/Uphametal2019tree")
matches <- match(mytree$tip.label, model3c$S, nomatch = 0)
not<-subset(mytree$tip.label, matches==0)
tree_pruned<-drop.tip(mytree, not)
mycombi <- comparative.data(phy = tree_pruned, data = myframe, names.col = S, vcv = TRUE, na.omit = FALSE, warn.dropped = TRUE)
model1 <- pgls(EQ ~ LogGS + SC + D + DB + HV + LogHR + LogGL + LogML + LogF + LogFR + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(EQ ~ HV + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(EQ ~ DB + LogF + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(EQ ~ HV + LogF + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(EQ ~ DB + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(EQ ~ HV + LogF, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(EQ ~ DB + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)

# RNS Models 

# Repeat social, ecological, social & ecological, and life history models as outlined above 

# Combined
library("ape")
library("geiger")
library("caper")
model4c <- read.csv("/Users/helen/OneDrive/Documents/Model4C.csv")
model4c$D <- as.factor(model4c$D)
myframe <- data.frame(model4c)
rownames(myframe) <- model4c$S
mytree <- read.nexus("/Users/helen/OneDrive/Documents/Uphametal2019tree")
matches <- match(mytree$tip.label, model4c$S, nomatch = 0)
not<-subset(mytree$tip.label, matches==0)
tree_pruned<-drop.tip(mytree, not)
mycombi <- comparative.data(phy = tree_pruned, data = myframe, names.col = S, vcv = TRUE, na.omit = FALSE, warn.dropped = TRUE)
model1 <- pgls(RNS ~ LogGS + SC + D + DB + HV + LogHR + LogGL + LogML + LogF + LogFR + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(RNS ~ LogHR + LogF + LogFR + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(RNS ~ LogHR + LogF + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(RNS ~ HV + LogF + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(RNS ~ DB + LogF + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(RNS ~ DB + LogF + LogFR, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(RNS ~ HV + LogF, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(RNS ~ DB + LogF + LogFR + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(RNS ~ HV + LogF + LogFR + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(RNS ~ HV + LogF + LogFR, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(RNS ~ DB + LogF, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(RNS ~ LogHR + LogF + LogFR, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(RNS ~ LogHR + LogF, data = mycombi, lambda = "ML" )
BIC(model1)


# RCS Models 

# Repeat social, ecological, social & ecological, and life history models as outlined above 

# Combined
library("ape")
library("geiger")
library("caper")
model5c <- read.csv("/Users/helen/OneDrive/Documents/Model5C.csv")
model5c$D <- as.factor(model5c$D)
myframe <- data.frame(model5c)
rownames(myframe) <- model5c$S
mytree <- read.nexus("/Users/helen/OneDrive/Documents/Uphametal2019tree")
matches <- match(mytree$tip.label, model5c$S, nomatch = 0)
not<-subset(mytree$tip.label, matches==0)
tree_pruned<-drop.tip(mytree, not)
mycombi <- comparative.data(phy = tree_pruned, data = myframe, names.col = S, vcv = TRUE, na.omit = FALSE, warn.dropped = TRUE)
model1 <- pgls(RCS ~ LogGS + SC + D + DB + HV + LogHR + LogGL + LogML + LogF + LogFR + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(RCS ~ LogGS + LogML, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(RCS ~ SC + LogML, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(RCS ~ SC + LogFR, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(RCS ~ SC + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(RCS ~ SC + LogF, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(RCS ~ SC + LogGL, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(RCS ~ LogGS + LogWA, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(RCS ~ LogGS + LogFR, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(RCS ~ LogGS + LogGL, data = mycombi, lambda = "ML" )
BIC(model1)
model1 <- pgls(RCS ~ LogGS + LogF, data = mycombi, lambda = "ML" )
BIC(model1)


# Neo Models 

# Repeat social, ecological, social & ecological, and life history models as outlined above 

# Combined
library("ape")
library("geiger")
library("caper")
model6c <- read.csv("/Users/helen/OneDrive/Documents/Model6C.csv")
model6c$D <- as.factor(model6c$D)
myframe <- data.frame(model6c)
rownames(myframe) <- model6c$S
mytree <- read.nexus("/Users/helen/OneDrive/Documents/Uphametal2019tree")
matches <- match(mytree$tip.label, model6c$S, nomatch = 0)
not<-subset(mytree$tip.label, matches==0)
tree_pruned<-drop.tip(mytree, not)
mycombi <- comparative.data(phy = tree_pruned, data = myframe, names.col = S, vcv = TRUE, na.omit = FALSE, warn.dropped = TRUE)
model1 <- pgls(LogNeoROB ~ LogGS + SC + D + DB + HV + LogHR + LogGL + LogML + LogF + LogFR + LogWA, data = mycombi, lambda = "ML")
BIC(model1)
model1 <- pgls(LogNeoROB ~ LogHR + LogFR, data = mycombi, lambda = "ML")
BIC(model1)
model1 <- pgls(LogNeoROB ~ LogHR + LogML + LogFR, data = mycombi, lambda = "ML")
BIC(model1)


# Cere Models 

# Repeat social, ecological, social & ecological, and life history models as outlined above 

# Combined
library("ape")
library("geiger")
library("caper")
model7c <- read.csv("/Users/helen/OneDrive/Documents/Model7C.csv")
model7c$D <- as.factor(model7c$D)
myframe <- data.frame(model7c)
rownames(myframe) <- model7c$S
mytree <- read.nexus("/Users/helen/OneDrive/Documents/Uphametal2019tree")
matches <- match(mytree$tip.label, model7c$S, nomatch = 0)
not<-subset(mytree$tip.label, matches==0)
tree_pruned<-drop.tip(mytree, not)
mycombi <- comparative.data(phy = tree_pruned, data = myframe, names.col = S, vcv = TRUE, na.omit = FALSE, warn.dropped = TRUE)
model1 <- pgls(LogCereROB ~ LogGS + SC + D + DB + HV + LogHR + LogGL + LogML + LogF + LogFR + LogWA, data = mycombi, lambda = "ML")
BIC(model1)
model1 <- pgls(LogCereROB ~ LogHR + LogGL + LogML + LogFR, data = mycombi, lambda = "ML")
BIC(model1)
model1 <- pgls(LogCereROB ~ LogHR + LogML + LogFR, data = mycombi, lambda = "ML")
BIC(model1)
model1 <- pgls(LogCereROB ~ LogHR + LogML + LogFR + LogWA, data = mycombi, lambda = "ML")
BIC(model1)


#All models run