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speciesCutPGLS.Rmd
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---
title: "cutHighPrevPGLS"
author: "Walker M"
date: "2024-07-04"
output:
html_document:
code_folding: hide
---
```{r}
library(nlme)
library(rms)
library(phytools)
library(geiger)
library(caper)
library(tidyverse)
library(cowplot)
library(ggrepel)
library(ggsci)
library(patchwork)
library(poolr)
library(rr2)
#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$minimum,...)) #Returns the final model fit
}
#read data
Data <- read.csv("min20-2022.05.16.csv")
View(Data)
exclude_names <- c("Opossum", "Ferret", "Jaguar", "Four-toed hedgehog", "Milk snake", "King snake", "Albino bull snake", "Snow leopard", "Pygmy sugar glider")
# Filter rows where 'common_name' is not in the list of 'exclude_names'
filtered_data <- Data[!Data$common_name %in% exclude_names, ]
Data<-filtered_data
```
adult mass neo
```{r}
#adult weight models
#adult weight neo
cutData <- Data[,c(5,9,10,11,13,38,42),drop=FALSE]
cutData[cutData$adult_weight == -1, ] <-NA
cutData <- na.omit(cutData)
tree <- read.tree("min20Fixed516.nwk")
cutData$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$Species)[rownames(cutData)]
#pgls model
adult.weight.neo<-pglsSEyPagel(NeoplasiaPrevalence~log10(adult_weight.g.),data=cutData,tree=pruned.tree,se=SE,method = "ML")
#grab r squared, lambda, and p values from summary
r.v.adult.weight.neo <- R2(phy = pruned.tree,adult.weight.neo)
r.v.adult.weight.neo <- format(r.v.adult.weight.neo[3])
r.v.adult.weight.neo <-signif(as.numeric(r.v.adult.weight.neo), digits= 2)
ld.v.adult.weight.neo<- summary(adult.weight.neo)$modelStruct$corStruct
ld.v.adult.weight.neo <- signif(ld.v.adult.weight.neo[1], digits = 2)
p.v.adult.weight.neo<-summary(adult.weight.neo)$tTable
p.v.adult.weight.neo<-signif(p.v.adult.weight.neo[2,4], digits = 2)
summary(adult.weight.neo)
ggplot(cutData, aes(y=NeoplasiaPrevalence*100, x=log10(adult_weight.g.)))+
scale_color_manual(values = c("Mammalia" = "#EB1D24", "Sauropsida"= "#008b45ff", "Amphibia"= "#3B4992ff" ),)+
scale_y_continuous(
limits = c(0,75),
breaks = c(0, 25,50,75),
labels = c(0, 25,50,75))+
geom_abline(intercept = coef(adult.weight.neo)[1]*100, slope = coef(adult.weight.neo)[2]*100,
color = 'grey',size = 1.2) +
theme_cowplot(12)+
theme(axis.title = element_text(size = 18))+
ylab("Neoplasia Prevalence (%)") +
xlab("(log10) Adult Body Mass (g)") +
geom_point(aes(colour= Clade, size = RecordsWithDenominators)) +
scale_size(name = "Total Necropsies",
breaks = c(20,100,200,300,477),
labels = c(20,100,200,300,477))+
geom_text_repel(aes(label=ifelse( NeoplasiaPrevalence > .3,as.character(common_name),'')),max.overlaps = Inf,size=5, direction = "y")+
guides(colour = guide_legend(override.aes = list(size=3))) +
labs(title = "A")+
theme(
plot.title = element_text(size = 20, face = "bold")) +
theme(legend.position = "bottom")+
labs(colour="Clade", size="Total Necropsies")
```
adult body mass mal
```{r}
#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)
#pgls model
adult.weight.mal<-pglsSEyPagel(MalignancyPrevalence~log10(adult_weight.g.),data=cutData,
tree=pruned.tree,se=SE,method="ML")
summary(adult.weight.mal)
#grab r squared, lambda, and p values from summary
r.v.adult.weight.mal <- R2(phy = pruned.tree,adult.weight.mal)
r.v.adult.weight.mal <- format(r.v.adult.weight.mal[3])
r.v.adult.weight.mal <-signif(as.numeric(r.v.adult.weight.mal), digits= 2)
ld.v.adult.weight.mal<- summary(adult.weight.mal)$modelStruct$corStruct
ld.v.adult.weight.mal <- signif(ld.v.adult.weight.mal[1], digits= 2)
p.v.adult.weight.mal<-summary(adult.weight.mal)$tTable
p.v.adult.weight.mal<-signif(p.v.adult.weight.mal[2,4], digits = 3)
summary(adult.weight.mal)
ggplot(cutData, aes(y=MalignancyPrevalence*100, x=log10(adult_weight.g.)))+
scale_color_manual(values = c("Mammalia" = "#EB1D24", "Sauropsida"= "#008b45ff", "Amphibia"= "#3B4992ff" ))+
scale_y_continuous(
limits = c(0,75),
breaks = c(0, 25,45,75),
labels = c(0, 25,45,75))+
coord_cartesian(xlim = c(log10(min(cutData$adult_weight.g.)),log10(max(cutData$adult_weight.g.))),
ylim = c(0,75),clip = "off")+
geom_abline(intercept = coef(adult.weight.mal)[1]*100, slope = coef(adult.weight.mal)[2]*100,
color = 'grey',size = 1.2) +
theme_cowplot(12)+
theme(axis.title = element_text(size = 18))+
ylab("Malignancy Prevalence (%)") +
xlab("(log10) Adult Body Mass (g)") +
geom_point(aes(colour= Clade, size = RecordsWithDenominators)) +
geom_text_repel(aes(label=ifelse(MalignancyPrevalence > .3,as.character(common_name),'')),max.overlaps = Inf,size=5, direction = "y")+
scale_size(name = "Total Necropsies",
breaks = c(20,100,200,300,477),
labels = c(20,100,200,300,477))+
guides(colour = guide_legend(override.aes = list(size=5))) +
labs(title = "A.1")+
theme(
plot.title = element_text(size = 20, face = "bold")) +
theme(legend.position = "bottom")+ labs(colour="Clade", size="Total Necropsies")+
guides(size=guide_legend())+
annotate("text", x=.42, y=50.3, label = "1", size = 7)
```
longevity neo
```{r}
cutData <- Data[,c(5,9,10,11,13,40,42),drop=FALSE]
cutData[cutData$max_longevity.months. < 0,] <-NA
cutData <- na.omit(cutData)
tree <- read.tree("min20Fixed516.nwk")
#view(cutData)
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)
#pgls model
longevity.neo<-pglsSEyPagel(NeoplasiaPrevalence~max_longevity.months.,data=cutData,
tree=pruned.tree,method="ML",se=SE)
summary(longevity.neo)
r.v.longneo <- R2(phy = pruned.tree,longevity.neo)
r.v.longneo <- format(r.v.longneo[3])
r.v.longneo <-signif(as.numeric(r.v.longneo), digits= 2)
ld.v.longneo<- summary(longevity.neo)$modelStruct$corStruct
ld.v.longneo <- signif(ld.v.longneo[1], digits = 2)
p.v.longneo<-summary(longevity.neo)$tTable
p.v.longneo<-signif(p.v.longneo[2,4], digits = 3)
longneo<-ggplot(cutData, aes(y=NeoplasiaPrevalence*100, x=log10(max_longevity.months.))) +
scale_color_manual(values = c("Mammalia" = "#EB1D24", "Sauropsida"= "#008b45ff", "Amphibia"= "#3B4992ff" ))+
scale_y_continuous(
limits = c(0,75),
breaks = c(0, 25,50,75),
labels = c(0, 25,50,75))+
geom_abline(intercept = coef(longevity.neo)[1]*100, slope = coef(longevity.neo)[2]*100,
color = 'grey',size = 1.2) +
theme_cowplot(12)+
theme(axis.title = element_text(size = 18))+
ylab("Neoplasia Prevalence (%)") +
xlab("(log10) Max Longevity (Mo)") +
geom_point(aes(colour= Clade, size = RecordsWithDenominators)) +
scale_size(name = "Total Necropsies",
breaks = c(20,100,200,300,477),
labels = c(20,100,200,300,477))+
geom_text_repel(aes(label=ifelse(NeoplasiaPrevalence > .3,as.character(common_name),'')),max.overlaps = Inf,size=5, direction = "y")+
guides(colour = guide_legend(override.aes = list(size=5))) +
labs(title = "B")+
theme(
plot.title = element_text(size = 20, face = "bold")) +
theme(legend.position = "bottom")+
labs(colour="Clade", size="Total Necropsies")
```
longevity mal
```{r}
cutData <- Data[,c(5,9,10,11,17,40,42),drop=FALSE]
cutData[cutData < 0] <-NA
cutData <- na.omit(cutData)
tree <- read.tree("min20Fixed516.nwk")
#view(cutData)
cutData$Species <- gsub(" ", "_", cutData$Species)
cutData$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$Species)[rownames(cutData)]
#view(cutData)
#pgls model
longevity.mal<-pglsSEyPagel(MalignancyPrevalence~log10(max_longevity.months.),data=cutData,
tree=pruned.tree,method="ML",se=SE)
summary(longevity.mal)
r.v.longmal <- R2(phy = pruned.tree,longevity.mal)
r.v.longmal <- format(r.v.longmal[3])
r.v.longmal <-signif(as.numeric(r.v.longmal), digits= 2)
ld.v.longmal<- summary(longevity.mal)$modelStruct$corStruct
ld.v.longmal <- signif(ld.v.longmal[1])
p.v.longmal<-summary(longevity.mal)$tTable
p.v.longmal<-signif(p.v.longmal[2,4], digits = 3)
ggplot(cutData, aes(y=MalignancyPrevalence*100, x=log10(max_longevity.months.))) +
scale_color_manual(values = c("Mammalia" = "#EB1D24", "Sauropsida"= "#008b45ff", "Amphibia"= "#3B4992ff" ))+
scale_y_continuous(
limits = c(0,75),
breaks = c(0, 25,50,75),
labels = c(0, 25,50,75))+
coord_cartesian(xlim = c(log10(min(cutData$max_longevity.months.)),log10(max(cutData$max_longevity.months.))),
ylim = c(0,75),clip = "off")+
geom_abline(intercept = coef(longevity.mal)[1]*100, slope = coef(longevity.mal)[2]*100,
color = 'grey',size = 1.2) +
theme_cowplot(12)+
theme(axis.title = element_text(size = 18))+
ylab("Malignancy Prevalence (%)") +
xlab("Max Longevity (Mo)") +
geom_point(aes(colour= Clade, size = RecordsWithDenominators)) +
geom_text_repel(aes(label=ifelse( MalignancyPrevalence > .3,as.character(common_name),'')),max.overlaps = Inf,size=5, direction = "y")+
guides(colour = guide_legend(override.aes = list(size=5))) +
labs(title = "B.2")+
theme(
plot.title = element_text(size = 20, face = "bold")) +
theme(legend.position = "bottom")+ labs(colour="Clade", size="Total Necropsies")+
guides(size=guide_legend())
```
gestation neo
```{r}
#gestation models
#gestation neo
cutData <- Data[,c(5,9,10,11,13,30,42),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)
#pgls model
gestation.neo<-pglsSEyPagel(NeoplasiaPrevalence~log10(Gestation.months.),data=cutData,
tree=pruned.tree,method="ML",se=SE)
summary(gestation.neo)
#grab r squared, lambda, and p values from summary
r.v.gestneo <- R2(phy = pruned.tree,gestation.neo)
r.v.gestneo <- format(r.v.gestneo[3])
r.v.gestneo<-signif(as.numeric(r.v.gestneo), digits= 2)
ld.v.gestneo<- summary(gestation.neo)$modelStruct$corStruct
ld.v.gestneo <- signif(ld.v.gestneo[1], digits = 2)
p.v.gestneo<-summary(gestation.neo)$tTable
p.v.gestneo<-signif(p.v.gestneo[2,4], digits = 3)
ggplot(cutData, aes(y=NeoplasiaPrevalence*100, x=log10(Gestation.months.))) +
scale_color_manual(values = c("Mammalia" = "#EB1D24", "Sauropsida"= "#008b45ff", "Amphibia"= "#3B4992ff" ))+
scale_y_continuous(
limits = c(0,75),
breaks = c(0, 25,45,75),
labels = c(0, 25,45,75))+
geom_abline(intercept = coef(gestation.neo)[1]*100, slope = coef(gestation.neo)[2]*100,
color = 'grey',size = 1.2) +
theme_cowplot(12)+
theme(axis.title = element_text(size = 18))+
ylab("Neoplasia Prevalence (%)") +
xlab("(log10) Gestation") +
geom_point(aes(colour= Clade, size = RecordsWithDenominators)) +
geom_text_repel(aes(label=ifelse(NeoplasiaPrevalence > .3,as.character(common_name),'')),max.overlaps = Inf,size=5, direction = "y")+
guides(colour = guide_legend(override.aes = list(size=5)))+
theme(
plot.title = element_text(size = 20, face = "bold")) +
theme(legend.position = "bottom")+ labs(colour="Clade", size="Total Necropsies")+
labs(title="A")
```
gestation mal
```{r}
#gestation mal
cutData <- Data[,c(5,9,10,11,17,30,42),drop=FALSE]
cutData[cutData$Gestation.months. < 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)
#pgls model
gestation.mal<-pglsSEyPagel(MalignancyPrevalence~log10(Gestation.months.),data=cutData,
tree=pruned.tree,method="ML",se=SE)
summary(gestation.mal)
#grab r squared, lambda, and p values from summary
r.v.gestmal <- R2(phy = pruned.tree,gestation.mal)
r.v.gestmal <- format(r.v.gestmal[3])
r.v.gestmal<-signif(as.numeric(r.v.gestmal), digits= 2)
ld.v.gestmal<- summary(gestation.mal)$modelStruct$corStruct
ld.v.gestmal<- signif(ld.v.gestmal[1], digits = 2)
p.v.gestmal<-summary(gestation.mal)$tTable
p.v.gestmal<-signif(p.v.gestmal[2,4], digits = 3)
ggplot(cutData, aes(y=MalignancyPrevalence*100, x=log10(Gestation.months.))) +
scale_color_manual(values = c("Mammalia" = "#EB1D24", "Sauropsida"= "#008b45ff", "Amphibia"= "#3B4992ff" ))+
scale_y_continuous(
limits = c(0,75),
breaks = c(0, 25,45,75),
labels = c(0, 25,45,75))+
geom_abline(intercept = coef(gestation.mal)[1]*100, slope = coef(gestation.mal)[2]*100,
color = 'grey',size = 1.2) +
theme_cowplot(12)+
theme(axis.title = element_text(size = 18))+
ylab("Malignancy Prevalence (%)") +
xlab("(log10) Gestation") +
geom_point(aes(colour= Clade, size = RecordsWithDenominators)) +
geom_text_repel(aes(label=ifelse(MalignancyPrevalence > .3,as.character(common_name),'')),max.overlaps = Inf,size=5, direction = "y")+
guides(colour = guide_legend(override.aes = list(size=5)))+
theme(
plot.title = element_text(size = 20, face = "bold")) +
theme(legend.position = "bottom")+ labs(colour="Clade", size="Total Necropsies")+
labs(title="B")
```
```{r}
#weight +gestation neo
cutData <- Data[,c(5,9,10,11,13,30,38,42),drop=FALSE]
cutData[cutData < 0] <-NA
cutData <- na.omit(cutData)
tree <- read.tree("min20Fixed516.nwk")
cutData$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$Species)[rownames(cutData)]
#view(cutData)
#pgls model
wgtgestation.neo<-pglsSEyPagel(NeoplasiaPrevalence~log10(Gestation.months.)+log10(adult_weight.g.),data=cutData,
tree=pruned.tree,method="ML",se=SE)
summary(wgtgestation.neo)
```
```{r}
#weight + gestation mal
cutData <- Data[,c(5,9,10,11,17,30,38,42),drop=FALSE]
cutData[cutData < 0] <-NA
cutData <- na.omit(cutData)
cutData <- na.omit(cutData)
tree <- read.tree("min20Fixed516.nwk")
cutData$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)
#pgls model
wgtgestation.mal<-pglsSEyPagel(MalignancyPrevalence~log10(Gestation.months.)+log10(adult_weight.g.),data=cutData,
tree=pruned.tree,method="ML",se=SE)
summary(wgtgestation.mal)
```