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min20VertGroup.R
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min20VertGroup.R
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library(nlme)
library(rms)
library(phytools)
library(geiger)
library(caper)
library(tidyverse)
library(cowplot)
library(ggrepel)
library(ggsci)
#make sure to run all of this before you get to work.
#pgls sey base (just run all of this)
modPgls.SEy = function (model, data, corClass = corBrownian, tree, se = NULL,
method = c("REML", "ML"), interval = c(0, 1000), corClassValue=1, sig2e=NULL, ...)
{
Call <- match.call()
corfunc <- corClass
spp <- rownames(data)
data <- cbind(data, spp)
if (is.null(se))
se <- setNames(rep(0, Ntip(tree)), tree$tip.label)[spp]
else se <- se[spp]
lk <- function(sig2e, data, tree, model, ve, corfunc, spp) {
tree$edge.length <- tree$edge.length * sig2e
ii <- sapply(1:Ntip(tree), function(x, e) which(e ==
x), e = tree$edge[, 2])
tree$edge.length[ii] <- tree$edge.length[ii] + ve[tree$tip.label]
vf <- diag(vcv(tree))[spp]
w <- varFixed(~vf)
COR <- corfunc(corClassValue, tree, form = ~spp, ...)
fit <- gls(model, data = cbind(data, vf), correlation = COR,
method = method, weights = w)
-logLik(fit)
}
if (is.null(sig2e)) {
fit <- optimize(lk, interval = interval, data = data, tree = tree,
model = model, ve = se^2, corfunc = corfunc, spp = spp)
sig2e=fit$minimum
}
tree$edge.length <- tree$edge.length * sig2e
ii <- sapply(1:Ntip(tree), function(x, e) which(e == x),
e = tree$edge[, 2])
tree$edge.length[ii] <- tree$edge.length[ii] + se[tree$tip.label]^2
vf <- diag(vcv(tree))[spp]
w <- varFixed(~vf)
obj <- gls(model, data = cbind(data, vf), correlation = corfunc(corClassValue,
tree, form = ~spp, ...), weights = w, method = method)
obj$call <- Call
obj$sig2e <- sig2e
obj
}
#Internal function
pglsSEyPagelToOptimizeLambda=function(lambda,model,data,tree,...) {
-logLik(modPgls.SEy(model=model,data=data,tree=tree,corClassValue=lambda,corClass=corPagel,fixed=T,...)) #Returns -logLikelihood of the pgls.SEy model with lambda fixed to the value of the lambda argument. sig2e will be optimized within modPgls.SEy unless given as an argument here
}
#Function intended for users
pglsSEyPagel=function(model, data, tree, lambdaInterval=c(0,1),...){
optimizedModel=optimize(pglsSEyPagelToOptimizeLambda,lambdaInterval,model=model,data=data,tree=tree,...) #Optimizes lambda in the lambdaInterval using the pglsSEyPagelToOptimizeLambda function
return(modPgls.SEy(model=model,data=data,tree=tree,corClass=corPagel,fixed=T,corClassValue=optimizedModel$minimum,...)) #Returns the final model fit
}
##squamata
#read in data and filter
Data <- read.csv("min20-2022.05.16.csv")
Data<- filter(Data, is.element(Orders, c("Squamata")))
View(Data)
#adult weight models
#adult weight models
#adult weight neo
cutData <- Data[,c(5,9,10,11,13,38,42),drop=FALSE]
cutData[cutData < 0] <-NA
cutData <- na.omit(cutData)
tree <- read.tree("min20Fixed516.nwk")
cutData$Species <- gsub(" ", "_", cutData$Species)
cutData$common_name<-gsub("_", "", cutData$common_name)
includedSpecies<-cutData$Species
pruned.tree<-drop.tip(
tree, setdiff(
tree$tip.label, includedSpecies))
pruned.tree <- keep.tip(pruned.tree,pruned.tree$tip.label)
cutData$Keep <- cutData$Species %in% pruned.tree$tip.label
cutData <- cutData[!(cutData$Keep==FALSE),]
rownames(cutData)<-cutData$Species
SE<-setNames(cutData$SE_simple,cutData$Species)[rownames(cutData)]
view(cutData)
adult.weight.neo<-pglsSEyPagel(NeoplasiaPrevalence~log10(adult_weight.g.),data=cutData,tree=pruned.tree,se=SE,method = "ML")
summary(adult.weight.neo)
#adult weight mal
cutData <- Data[,c(5,9,10,11,17,38,42),drop=FALSE]
cutData[cutData < 0] <-NA
cutData <- na.omit(cutData)
tree <- read.tree("min20Fixed516.nwk")
cutData$Species <- gsub(" ", "_", cutData$Species)
cutData$common_name<-gsub("_", "", cutData$common_name)
includedSpecies <- cutData$Species
pruned.tree<-drop.tip(
tree, setdiff(
tree$tip.label, includedSpecies))
pruned.tree <- keep.tip(pruned.tree,pruned.tree$tip.label)
cutData$Keep <- cutData$Species %in% pruned.tree$tip.label
cutData <- cutData[!(cutData$Keep==FALSE),]
rownames(cutData)<-cutData$Species
SE<-setNames(cutData$SE_simple,cutData$Species)[rownames(cutData)]
view(cutData)
adult.weight.mal<-pglsSEyPagel(MalignancyPrevalence~log10(adult_weight.g.),data=cutData,
tree=pruned.tree,se=SE,method="ML")
summary(adult.weight.mal)
### Longevity model
#longevity neo
cutData <- Data[,c(5,9,10,11,13,40,42),drop=FALSE]
cutData[cutData < 0] <-NA
cutData <- na.omit(cutData)
tree <- read.tree("min20Fixed516.nwk")
view(cutData)
cutData$Species <- gsub(" ", "_", cutData$Species)
includedSpecies <- cutData$Species
pruned.tree<-drop.tip(
tree, setdiff(
tree$tip.label, includedSpecies))
pruned.tree <- keep.tip(pruned.tree,pruned.tree$tip.label)
cutData$Keep <- cutData$Species %in% pruned.tree$tip.label
cutData <- cutData[!(cutData$Keep==FALSE),]
rownames(cutData)<-cutData$Species
SE<-setNames(cutData$SE_simple,cutData$Species)[rownames(cutData)]
view(cutData)
longevity.neo<-pglsSEyPagel(NeoplasiaPrevalence~max_longevity.months.,data=cutData,
tree=pruned.tree,method="ML",se=SE)
summary(longevity.neo)
#longevity mal
cutData <- Data[,c(5,9,10,11,17,40,42),drop=FALSE]
cutData[cutData < 0] <-NA
cutData <- na.omit(cutData)
tree <- read.tree("min20Fixed516.nwk")
view(cutData)
cutData$Species <- gsub(" ", "_", cutData$Species)
includedSpecies <- cutData$Species
pruned.tree<-drop.tip(
tree, setdiff(
tree$tip.label, includedSpecies))
pruned.tree <- keep.tip(pruned.tree,pruned.tree$tip.label)
cutData$Keep <- cutData$Species %in% pruned.tree$tip.label
cutData <- cutData[!(cutData$Keep==FALSE),]
rownames(cutData)<-cutData$Species
SE<-setNames(cutData$SE_simple,cutData$Species)[rownames(cutData)]
view(cutData)
longevity.mal<-pglsSEyPagel(MalignancyPrevalence~(max_longevity.months.),data=cutData,
tree=pruned.tree,method="ML",se=SE)
summary(longevity.mal)
##Aves
#read in data and filter
Data <- read.csv("min20-2022.05.16.csv")
Data<- filter(Data, is.element(Class, c("Aves")))
View(Data)
#adult weight models
#adult weight models
#adult weight neo
cutData <- Data[,c(5,9,10,11,13,38,42),drop=FALSE]
cutData[cutData < 0] <-NA
cutData <- na.omit(cutData)
tree <- read.tree("min20Fixed516.nwk")
cutData$Species <- gsub(" ", "_", cutData$Species)
cutData$common_name<-gsub("_", "", cutData$common_name)
includedSpecies<-cutData$Species
pruned.tree<-drop.tip(
tree, setdiff(
tree$tip.label, includedSpecies))
pruned.tree <- keep.tip(pruned.tree,pruned.tree$tip.label)
cutData$Keep <- cutData$Species %in% pruned.tree$tip.label
cutData <- cutData[!(cutData$Keep==FALSE),]
rownames(cutData)<-cutData$Species
SE<-setNames(cutData$SE_simple,cutData$Species)[rownames(cutData)]
view(cutData)
adult.weight.neo<-pglsSEyPagel(NeoplasiaPrevalence~log10(adult_weight.g.),data=cutData,tree=pruned.tree,se=SE,method = "ML")
summary(adult.weight.neo)
#adult weight mal
cutData <- Data[,c(5,9,10,11,17,38,42),drop=FALSE]
cutData[cutData < 0] <-NA
cutData <- na.omit(cutData)
tree <- read.tree("min20Fixed516.nwk")
cutData$Species <- gsub(" ", "_", cutData$Species)
cutData$common_name<-gsub("_", "", cutData$common_name)
includedSpecies <- cutData$Species
pruned.tree<-drop.tip(
tree, setdiff(
tree$tip.label, includedSpecies))
pruned.tree <- keep.tip(pruned.tree,pruned.tree$tip.label)
cutData$Keep <- cutData$Species %in% pruned.tree$tip.label
cutData <- cutData[!(cutData$Keep==FALSE),]
rownames(cutData)<-cutData$Species
SE<-setNames(cutData$SE_simple,cutData$Species)[rownames(cutData)]
view(cutData)
adult.weight.mal<-pglsSEyPagel(MalignancyPrevalence~log10(adult_weight.g.),data=cutData,
tree=pruned.tree,se=SE,method="ML")
summary(adult.weight.mal)
### Longevity model
#longevity neo
cutData <- Data[,c(5,9,10,11,13,40,42),drop=FALSE]
cutData[cutData < 0] <-NA
cutData <- na.omit(cutData)
tree <- read.tree("min20Fixed516.nwk")
view(cutData)
cutData$Species <- gsub(" ", "_", cutData$Species)
includedSpecies <- cutData$Species
pruned.tree<-drop.tip(
tree, setdiff(
tree$tip.label, includedSpecies))
pruned.tree <- keep.tip(pruned.tree,pruned.tree$tip.label)
cutData$Keep <- cutData$Species %in% pruned.tree$tip.label
cutData <- cutData[!(cutData$Keep==FALSE),]
rownames(cutData)<-cutData$Species
SE<-setNames(cutData$SE_simple,cutData$Species)[rownames(cutData)]
view(cutData)
longevity.neo<-pglsSEyPagel(NeoplasiaPrevalence~max_longevity.months.,data=cutData,
tree=pruned.tree,method="ML",se=SE)
summary(longevity.neo)
#longevity mal
cutData <- Data[,c(5,9,10,11,17,40,42),drop=FALSE]
cutData[cutData < 0] <-NA
cutData <- na.omit(cutData)
tree <- read.tree("min20Fixed516.nwk")
view(cutData)
cutData$Species <- gsub(" ", "_", cutData$Species)
includedSpecies <- cutData$Species
pruned.tree<-drop.tip(
tree, setdiff(
tree$tip.label, includedSpecies))
pruned.tree <- keep.tip(pruned.tree,pruned.tree$tip.label)
cutData$Keep <- cutData$Species %in% pruned.tree$tip.label
cutData <- cutData[!(cutData$Keep==FALSE),]
rownames(cutData)<-cutData$Species
SE<-setNames(cutData$SE_simple,cutData$Species)[rownames(cutData)]
view(cutData)
longevity.mal<-pglsSEyPagel(MalignancyPrevalence~(max_longevity.months.),data=cutData,
tree=pruned.tree,method="ML",se=SE)
summary(longevity.mal)
#turtles
#read in data and filter
Data <- read.csv("min20-2022.05.16.csv")
Data<- filter(Data, is.element(Orders, c("Testudines")))
View(Data)
#adult weight models
#adult weight models
#adult weight neo
cutData <- Data[,c(5,9,10,11,13,38,42),drop=FALSE]
cutData[cutData < 0] <-NA
cutData <- na.omit(cutData)
tree <- read.tree("min20Fixed516.nwk")
cutData$Species <- gsub(" ", "_", cutData$Species)
cutData$common_name<-gsub("_", "", cutData$common_name)
includedSpecies<-cutData$Species
pruned.tree<-drop.tip(
tree, setdiff(
tree$tip.label, includedSpecies))
pruned.tree <- keep.tip(pruned.tree,pruned.tree$tip.label)
cutData$Keep <- cutData$Species %in% pruned.tree$tip.label
cutData <- cutData[!(cutData$Keep==FALSE),]
rownames(cutData)<-cutData$Species
SE<-setNames(cutData$SE_simple,cutData$Species)[rownames(cutData)]
view(cutData)
adult.weight.neo<-pglsSEyPagel(NeoplasiaPrevalence~log10(adult_weight.g.),data=cutData,tree=pruned.tree,se=SE,method = "ML")
summary(adult.weight.neo)
#adult weight mal
cutData <- Data[,c(5,9,10,11,17,38,42),drop=FALSE]
cutData[cutData < 0] <-NA
cutData <- na.omit(cutData)
tree <- read.tree("min20Fixed516.nwk")
cutData$Species <- gsub(" ", "_", cutData$Species)
cutData$common_name<-gsub("_", "", cutData$common_name)
includedSpecies <- cutData$Species
pruned.tree<-drop.tip(
tree, setdiff(
tree$tip.label, includedSpecies))
pruned.tree <- keep.tip(pruned.tree,pruned.tree$tip.label)
cutData$Keep <- cutData$Species %in% pruned.tree$tip.label
cutData <- cutData[!(cutData$Keep==FALSE),]
rownames(cutData)<-cutData$Species
SE<-setNames(cutData$SE_simple,cutData$Species)[rownames(cutData)]
view(cutData)
adult.weight.mal<-pglsSEyPagel(MalignancyPrevalence~log10(adult_weight.g.),data=cutData,
tree=pruned.tree,se=SE,method="ML")
summary(adult.weight.mal)
### Longevity model
#longevity neo
cutData <- Data[,c(5,9,10,11,13,40,42),drop=FALSE]
cutData[cutData < 0] <-NA
cutData <- na.omit(cutData)
tree <- read.tree("min20Fixed516.nwk")
view(cutData)
cutData$Species <- gsub(" ", "_", cutData$Species)
includedSpecies <- cutData$Species
pruned.tree<-drop.tip(
tree, setdiff(
tree$tip.label, includedSpecies))
pruned.tree <- keep.tip(pruned.tree,pruned.tree$tip.label)
cutData$Keep <- cutData$Species %in% pruned.tree$tip.label
cutData <- cutData[!(cutData$Keep==FALSE),]
rownames(cutData)<-cutData$Species
SE<-setNames(cutData$SE_simple,cutData$Species)[rownames(cutData)]
view(cutData)
longevity.neo<-pglsSEyPagel(NeoplasiaPrevalence~max_longevity.months.,data=cutData,
tree=pruned.tree,method="ML",se=SE)
summary(longevity.neo)
#longevity mal
cutData <- Data[,c(5,9,10,11,17,40,42),drop=FALSE]
cutData[cutData < 0] <-NA
cutData <- na.omit(cutData)
tree <- read.tree("min20Fixed516.nwk")
view(cutData)
cutData$Species <- gsub(" ", "_", cutData$Species)
includedSpecies <- cutData$Species
pruned.tree<-drop.tip(
tree, setdiff(
tree$tip.label, includedSpecies))
pruned.tree <- keep.tip(pruned.tree,pruned.tree$tip.label)
cutData$Keep <- cutData$Species %in% pruned.tree$tip.label
cutData <- cutData[!(cutData$Keep==FALSE),]
rownames(cutData)<-cutData$Species
SE<-setNames(cutData$SE_simple,cutData$Species)[rownames(cutData)]
view(cutData)
longevity.mal<-pglsSEyPagel(MalignancyPrevalence~(max_longevity.months.),data=cutData,
tree=pruned.tree,method="ML",se=SE)
summary(longevity.mal)