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min20mam.R
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min20mam.R
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library(nlme)
library(rms)
library(phytools)
library(geiger)
library(caper)
library(tidyverse)
library(cowplot)
library(ggrepel)
library(ggsci)
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$minimum,...)) #Returns the final model fit
}
#read data
Data <- read.csv("min20-2022.05.16.csv")
Data<- filter(Data, is.element(Clade, c("Mammalia")))
View(Data)
#adult weight models
#adult weight neo
cutData <- Data[,c(5,9,10,11,13,38,42),drop=FALSE]
cutData[cutData < 0] <-NA
cutData <- na.omit(cutData)
tree <- read.tree("min20Fixed516.nwk")
cutData$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$Species)[rownames(cutData)]
view(cutData)
adult.weight.neo<-pglsSEyPagel(NeoplasiaPrevalence~log10(adult_weight.g.),data=cutData,tree=pruned.tree,se=SE,method = "ML")
summary(adult.weight.neo)
#grab r squared, p value, and lambda from summary
r.v.adult.weight.neo <- R2(phy = pruned.tree,adult.weight.neo)
r.v.adult.weight.neo <- format(r.v.adult.weight.neo[3])
r.v.adult.weight.neo <-signif(as.numeric(r.v.adult.weight.neo), digits= 2)
ld.v.adult.weight.neo<- summary(adult.weight.neo)$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)
#plot
ggplot(cutData, aes(y=NeoplasiaPrevalence*100, x=log10(adult_weight.g.)))+
scale_color_manual(values = c("Mammalia" = "#631879FF" ),)+
scale_y_continuous(
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 Weight (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 = "Neoplasia vs. Adult Weight",
subtitle =bquote(p-value:.(p.v.adult.weight.neo)~R^2:.(r.v.adult.weight.neo)~Lambda:.(ld.v.adult.weight.neo))) +
theme(
plot.title = element_text(size = 20, face = "bold")) +
theme(legend.position = "bottom")+
labs(colour="Clade", size="Total Necropsies")+
coord_cartesian(xlim = c(log10(min(cutData$adult_weight.g.)),log10(max(cutData$adult_weight.g.))),
ylim = c(0,75),clip = "off")+
annotate("text", x=.42, y=83.8, label = "17", size = 7)
ggsave(filename='S17wgtneo.pdf', width=13, height=10, limitsize=FALSE,bg="white")
#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)
#grab r squared, p value, and lambda 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)$tTable
p.v.adult.weight.mal<-signif(p.v.adult.weight.mal[2,4], digits = 3)
#plot
ggplot(cutData, aes(y=MalignancyPrevalence*100, x=log10(adult_weight.g.)))+
scale_color_manual(values = c("Mammalia" = "#631879FF"))+
scale_y_continuous(
breaks = c(0, 25,45),
labels = c(0, 25,45))+
coord_cartesian(xlim = c(log10(min(cutData$adult_weight.g.)),log10(max(cutData$adult_weight.g.))),
ylim = c(0,45),clip = "off")+
geom_abline(intercept = coef(adult.weight.mal)[1]*100, slope = coef(adult.weight.mal)[2]*100,
color = 'grey',size = 1.2) +
theme_cowplot(12)+
theme(axis.title = element_text(size = 18))+
ylab("Malignancy Prevalence (%)") +
xlab("(log10) Adult Weight (g)") +
geom_point(aes(colour= Clade, size = RecordsWithDenominators)) +
geom_text_repel(aes(label=ifelse(MalignancyPrevalence > .3,as.character(common_name),'')),max.overlaps = Inf,size=5, direction = "y")+
labs(title = "Malignancy Prevalence vs. Adult Weight",
subtitle =bquote(p-value:.(p.v.adult.weight.mal)~R^2:.(r.v.adult.weight.mal)~Lambda:.(ld.v.adult.weight.mal))) +
guides(colour = FALSE) +
theme(
plot.title = element_text(size = 20, face = "bold")) +
theme(legend.position = "bottom")+ labs(colour="Clade", size="Total Necropsies")+
guides(size=guide_legend())+
annotate("text", x=.42, y=50.2, label = "18", size = 7)
ggsave(filename='S18wgtmal.pdf', width=13, height=10, limitsize=FALSE,bg="white")
#gestation models
#gestation neo
cutData <- Data[,c(5,9,10,11,13,30,42),drop=FALSE]
cutData[cutData < 0] <-NA
cutData <- na.omit(cutData)
tree <- read.tree("min20Fixed516.nwk")
cutData$Species <- gsub(" ", "_", cutData$Species)
includedSpecies <- cutData$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$Species)[rownames(cutData)]
view(cutData)
gestation.neo<-pglsSEyPagel(NeoplasiaPrevalence~log10(Gestation.months.),data=cutData,
tree=pruned.tree,method="ML",se=SE)
summary(gestation.neo)
#grab r squared, p value, and lambda from summary
r.v.gestneo <- R2(phy = pruned.tree,gestation.neo)
r.v.gestneo <- format(r.v.gestneo[3])
r.v.gestneo<-signif(as.numeric(r.v.gestneo), digits= 2)
ld.v.gestneo<- summary(gestation.neo)$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)
#plot
ggplot(cutData, aes(y=NeoplasiaPrevalence*100, x=log10(Gestation.months.))) +
scale_color_manual(values = c("Mammalia" = "#631879FF"))+
scale_y_continuous(
breaks = c(0, 25,50,75),
labels = c(0, 25,50,75))+
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 (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")+
labs(title = "Neoplasia Prevalence vs. Gestation in Mammals",
subtitle =bquote(p-value:.(p.v.gestneo)~R^2:.(r.v.gestneo)~Lambda:.(ld.v.gestneo))) +
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")+
coord_cartesian(xlim = c(min(log10(cutData$Gestation.months.)),max(log10(cutData$Gestation.months.))),
ylim = c(0,75),clip = "off")+
annotate("text", x=-.53, y=83.8, label = "19", size = 7)
ggsave(filename='S19gestneomam.pdf', width=13, height=10, limitsize=FALSE,bg="white")
#gestation mal
cutData <- Data[,c(5,9,10,11,17,30,42),drop=FALSE]
cutData[cutData < 0] <-NA
cutData <- na.omit(cutData)
tree <- read.tree("min20Fixed516.nwk")
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)
gestation.mal<-pglsSEyPagel(MalignancyPrevalence~log10(Gestation.months.),data=cutData,
tree=pruned.tree,method="ML",se=SE)
summary(gestation.mal)
#grab r squared, p value, and lambda 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.gestneo), 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)$tTable
p.v.gestmal<-signif(p.v.gestmal[2,4], digits = 3)
#plot
ggplot(cutData, aes(y=MalignancyPrevalence*100, x=log10(Gestation.months.))) +
scale_color_manual(values = c("Mammalia" = "#631879FF"))+
scale_y_continuous(
breaks = c(0, 25,45),
labels = c(0, 25,45))+
coord_cartesian(xlim = c(log10(min(cutData$Gestation.months.)),log10(max(cutData$Gestation.months.))),
ylim = c(0,45),clip = "off")+
geom_abline(intercept = coef(gestation.mal)[1]*100, slope = coef(gestation.mal)[2]*100,
color = 'grey',size = 1.2) +
theme_cowplot(12)+
theme(axis.title = element_text(size = 18))+
ylab("Malignancy Prevalence (%)") +
xlab("(log10) Gestation (Mo)") +
geom_point(aes(colour= Clade, size = RecordsWithDenominators)) +
scale_size(name = "Total Necropsies",
breaks = c(20,100,200,300,477),
labels = c(20,100,200,300,477))+
geom_text_repel(aes(label=ifelse(MalignancyPrevalence > .3,as.character(common_name),'')),max.overlaps = Inf,size=5, direction = "y")+
labs(title = "Malignancy Prevalence vs. Gestation in Mammals",
subtitle =bquote(p-value:.(p.v.gestmal)~R^2:.(r.v.gestmal)~Lambda:.(ld.v.gestmal))) +
guides(colour = guide_legend(override.aes = list(size=5))) +
theme(
plot.title = element_text(size = 20, face = "bold")) +
theme(legend.position = "bottom")+ labs(colour="Clade", size="Total Necropsies")+
annotate("text", x=-.53, y=50.2, label = "20", size = 7)
ggsave(filename='S20gestmalmam.pdf', width=13, height=10, limitsize=FALSE,bg="white")
#litter size models
#litter size neo
cutData <- Data[,c(5,9,10,11,13,33,42),drop=FALSE]
cutData[cutData < 0] <-NA
cutData <- na.omit(cutData)
tree <- read.tree("min20Fixed516.nwk")
cutData$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$Species)[rownames(cutData)]
view(cutData)
litter.neo<-pglsSEyPagel(NeoplasiaPrevalence~log10(litter_size),data=cutData,
tree=pruned.tree,method="ML",se=SE)
summary(litter.neo)
#grab r squared, p value, and lambda from summary
r.v.litneo <- R2(phy = pruned.tree,litter.neo)
r.v.litneo <- format(r.v.litneo[3])
r.v.litneo <-signif(as.numeric(r.v.litneo), digits= 2)
ld.v.litneo<- summary(litter.neo)$modelStruct$corStruct
ld.v.litneo <- signif(ld.v.litneo[1], digits = 2)
p.v.litneo<-summary(litter.neo)$tTable
p.v.litneo<-signif(p.v.litneo[2,4], digits = 3)
#plot
ggplot(cutData, aes(y=NeoplasiaPrevalence*100, x=log10(litter_size))) +
scale_color_manual(values = c("Mammalia" = "#631879FF"))+
scale_y_continuous(
breaks = c(0, 25,50,75),
labels = c(0, 25,50,75))+
geom_abline(intercept = coef(litter.neo)[1]*100, slope = coef(litter.neo)[2]*100,
color = 'grey',size = 1.2) +
theme_cowplot(12)+
theme(axis.title = element_text(size = 18))+
ylab("Neoplasia Prevalence (%)") +
xlab("(log10) Litter Size") +
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")+
labs(title = "Neoplasia Prevalence vs. Litter Size in Mammals",
subtitle =bquote(p-value:.(p.v.litneo)~R^2:.(r.v.litneo)~Lambda:.(ld.v.litneo))) +
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")+
coord_cartesian(xlim = c(log10(min(cutData$litter_size)),log10(max(cutData$litter_size))),
ylim = c(0,75),clip = "off")+
annotate("text", x=-.13, y=83.8, label = "21", size = 7)
ggsave(filename='S21litneomam.pdf', width=13, height=10, limitsize=FALSE,bg="white")
#litter size mal
cutData <- Data[,c(5,9,10,11,17,33,42),drop=FALSE]
cutData[cutData < 0] <-NA
cutData <- na.omit(cutData)
tree <- read.tree("min20Fixed516.nwk")
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)
litter.mal <- pglsSEyPagel(MalignancyPrevalence~log10(litter_size),data=cutData,
tree=pruned.tree,method="ML",se=SE)
summary(litter.mal)
#grab r squared, p value, and lambda from summary
r.v.litmal <- R2(phy = pruned.tree,litter.mal)
r.v.litmal <- format(r.v.litmal[3])
r.v.litmal <-signif(as.numeric(r.v.litmal), digits= 2)
ld.v.litmal<- summary(litter.mal)$modelStruct$corStruct
ld.v.litmal <- signif(ld.v.litmal[1], digits = 2)
p.v.litmal<-summary(litter.mal)$tTable
p.v.litmal<-signif(p.v.litmal[2,4], digits = 3)
#plot
ggplot(cutData, aes(y=MalignancyPrevalence*100, x=log10(litter_size))) +
scale_color_manual(values = c("Mammalia" = "#631879FF"))+
scale_y_continuous(
breaks = c(0, 25,45),
labels = c(0, 25,45))+
coord_cartesian(xlim = c(log10(min(cutData$litter_size)),log10(max(cutData$litter_size))),
ylim = c(0,45),clip = "off")+
geom_abline(intercept = coef(litter.mal)[1]*100, slope = coef(litter.mal)[2]*100,
color = 'grey',size = 1.2) +
theme_cowplot(12)+
theme(axis.title = element_text(size = 18))+
ylab("Malignancy Prevalence (%)") +
xlab("(log10) Litter Size") +
geom_point(aes(colour= Clade, size = RecordsWithDenominators)) +
scale_size(name = "Total Necropsies",
breaks = c(20,100,200,300,477),
labels = c(20,100,200,300,477))+
geom_text_repel(aes(label=ifelse(MalignancyPrevalence > .3,as.character(common_name),'')),max.overlaps = Inf,size=5, direction = "y")+
labs(title = "Malignancy Prevalence vs. Litter Size in Mammals",
subtitle =bquote(p-value:.(p.v.litmal)~R^2:.(r.v.litmal)~Lambda:.(ld.v.litmal))) +
guides(colour = guide_legend(override.aes = list(size=5))) +
theme(
plot.title = element_text(size = 20, face = "bold")) +
theme(legend.position = "bottom")+ labs(colour="Clade", size="Total Necropsies")+
annotate("text", x=-.13, y=50.3, label = "22", size = 7)
ggsave(filename='S22litmalmam.pdf', width=13, height=10, limitsize=FALSE,bg="white")
### Longevity model
#longevity neo
cutData <- Data[,c(5,9,10,11,13,40,42),drop=FALSE]
cutData[cutData < 0] <-NA
cutData <- na.omit(cutData)
tree <- read.tree("min20Fixed516.nwk")
view(cutData)
cutData$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$Species)[rownames(cutData)]
view(cutData)
longevity.neo<-pglsSEyPagel(NeoplasiaPrevalence~max_longevity.months.,data=cutData,
tree=pruned.tree,method="ML",se=SE)
summary(longevity.neo)
#grab r squared, p value, and lambda from summary
r.v.longneo <- R2(phy = pruned.tree,longevity.neo)
r.v.longneo <- format(r.v.longneo[3])
r.v.longneo <-signif(as.numeric(r.v.longneo), digits= 2)
ld.v.longneo<- summary(longevity.neo)$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)
#plot
ggplot(cutData, aes(y=NeoplasiaPrevalence*100, x=log10(max_longevity.months.))) +
scale_color_manual(values = c("Mammalia" = "#631879FF"))+
scale_y_continuous(
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.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")+
labs(title = "Neoplasia Prevalence vs. Max Longevity in Mammals",
subtitle =bquote(p-value:.(p.v.longneo)~R^2:.(r.v.longneo)~Lambda:.(ld.v.longneo))) +
guides(colour = FALSE) +
theme(
plot.title = element_text(size = 20, face = "bold")) +
theme(legend.position = "bottom")+
labs(colour="Clade", size="Total Necropsies")+
annotate("text", x=1.565, y=83.8, label = "23", size = 7)
ggsave(filename='S23longneomam.pdf', width=13, height=10, limitsize=FALSE,bg="white")
#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)
#grab r squared, p value, and lambda from summary
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)$tTable
p.v.longmal<-signif(p.v.longmal[2,4], digits = 3)
#plot
ggplot(cutData, aes(y=MalignancyPrevalence*100, x=log10(max_longevity.months.))) +
scale_color_manual(values = c("Mammalia" = "#631879FF"))+
scale_y_continuous(
breaks = c(0, 25,45),
labels = c(0, 25,45))+
coord_cartesian(xlim = c(log10(min(cutData$max_longevity.months.)),log10(max(cutData$max_longevity.months.))),
ylim = c(0,45),clip = "off")+
geom_abline(intercept = coef(longevity.mal)[1]*100, slope = coef(longevity.mal)[2]*100,
color = 'grey',size = 1.2) +
theme_cowplot(12)+
theme(axis.title = element_text(size = 18))+
ylab("Malignancy Prevalence (%)") +
xlab("Max Longevity (Mo)") +
geom_point(aes(colour= Clade, size = RecordsWithDenominators)) +
scale_size(name = "Total Necropsies",
breaks = c(20,100,200,300,477),
labels = c(20,100,200,300,477))+
geom_text_repel(aes(label=ifelse(MalignancyPrevalence > .3,as.character(common_name),'')),max.overlaps = Inf,size=5, direction = "y")+
labs(title = "Malignancy Prevalence vs. Max Longevity in Mammals",
subtitle =bquote(p-value:.(p.v.longmal)~R^2:.(r.v.longmal)~Lambda:.(ld.v.longmal))) +
guides(colour = guide_legend(override.aes = list(size=5))) +
theme(
plot.title = element_text(size = 20, face = "bold")) +
theme(legend.position = "bottom")+ labs(colour="Clade", size="Total Necropsies")+
annotate("text", x=1.565, y=50.2, label = "24", size = 7)
ggsave(filename='S24longmalmam.pdf', width=13, height=10, limitsize=FALSE,bg="white")
##BMR models
#bmr neo
cutData <- Data[,c(5,9,10,11,13,41,42),drop=FALSE]
cutData[cutData < 0] <-NA
cutData <- na.omit(cutData)
tree <- read.tree("min20Fixed516.nwk")
view(cutData)
cutData$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$Species)[rownames(cutData)]
view(cutData)
BMR.neo<-pglsSEyPagel(NeoplasiaPrevalence~log10(metabolic_rate),data=cutData,
tree=pruned.tree,method="ML",se=SE)
summary(BMR.neo)
#grab r squared, p value, and lambda from summary
r.v.bmrneo <- R2(phy = pruned.tree,BMR.neo)
r.v.bmrneo <- format(r.v.bmrneo[3])
r.v.bmrneo <-signif(as.numeric(r.v.bmrneo), digits= 2)
ld.v.bmrneo<- summary(BMR.neo)$modelStruct$corStruct
ld.v.bmrneo <- signif(ld.v.bmrneo[1], digits = 2)
p.v.bmrneo<-summary(BMR.neo)$tTable
p.v.bmrneo<-signif(p.v.bmrneo[2,4], digits = 3)
#plot
ggplot(cutData, aes(y=NeoplasiaPrevalence*100, x=log10(metabolic_rate))) +
scale_color_manual(values = c("Mammalia" = "#631879FF"))+
scale_y_continuous(
breaks = c(0, 25,50,75),
labels = c(0, 25,50,75))+
geom_abline(intercept = coef(BMR.neo)[1]*100, slope = coef(BMR.neo)[2]*100,
color = 'grey',size = 1.2) +
theme_cowplot(12)+
theme(axis.title = element_text(size = 18))+
ylab("Neoplasia Prevalence (%)") +
xlab("(log10) Basal Metabolic Rate") +
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")+
labs(title = "Neoplasia Prevalence vs. Metabolic Rate in Mammals",
subtitle =bquote(p-value:.(p.v.bmrneo)~R^2:.(r.v.bmrneo)~Lambda:.(ld.v.bmrneo))) +
guides(colour = guide_legend(override.aes = list(size=5))) +
theme(
plot.title = element_text(size = 20, face = "bold")) +
theme(legend.position = "bottom")+ labs(size="Total Necropsies")+guides(colour=FALSE)+
coord_cartesian(xlim = c(log10(min(cutData$metabolic_rate)),log10(max(cutData$metabolic_rate))),
ylim = c(0,75),clip = "off")+
annotate("text", x=.9, y=83.8, label = "25", size = 7)
ggsave(filename='S25bmrneomam.pdf', width=13, height=10, limitsize=FALSE,bg="white")
#bmr mal
cutData <- Data[,c(5,9,10,11,17,41,42),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)
BMR.mal<-pglsSEyPagel(MalignancyPrevalence~log10(metabolic_rate),data=cutData,
tree=pruned.tree,method="ML",se=SE)
summary(BMR.mal)
#grab r squared, p value, and lambda from summary
r.v.bmrmal <- R2(phy = pruned.tree,BMR.mal)
r.v.bmrmal <- format(r.v.bmrmal[3])
r.v.bmrmal <-signif(as.numeric(r.v.bmrmal), digits= 2)
ld.v.bmrmal<- summary(BMR.mal)$modelStruct$corStruct
ld.v.bmrmal <- signif(ld.v.bmrmal[1], digits = 2)
p.v.bmrmal<-summary(BMR.mal)$tTable
p.v.bmrmal<-signif(p.v.bmrmal[2,4], digits = 3)
#plot
ggplot(cutData, aes(y=MalignancyPrevalence*100, x=log10(metabolic_rate)))+
scale_color_manual(values = c("Mammalia" = "#631879FF"))+
scale_y_continuous(
breaks = c(0, 25,45),
labels = c(0, 25,45))+
coord_cartesian(xlim = c(log10(min(cutData$metabolic_rate)),log10(max(cutData$metabolic_rate))),
ylim = c(0,45),clip = "off")+
geom_abline(intercept = coef(BMR.mal)[1]*100, slope = coef(BMR.mal)[2]*100,
color = 'grey',size = 1.2) +
theme_cowplot(12)+
theme(axis.title = element_text(size = 18))+
ylab("Malignancy Prevalence (%)") +
xlab("(log10) Basal Metabolic Rate") +
geom_point(aes(colour= Clade, size = RecordsWithDenominators)) +
scale_size(name = "Total Necropsies",
breaks = c(20,100,200,300,477),
labels = c(20,100,200,300,477))+
geom_text_repel(aes(label=ifelse(MalignancyPrevalence > .3,as.character(common_name),'')),max.overlaps = Inf,size=5, direction = "y")+
labs(title = "Malignancy Prevalence vs. Metabolic Rate in Mammals",
subtitle =bquote(p-value:.(p.v.bmrmal)~R^2:.(r.v.bmrmal)~Lambda:.(ld.v.bmrmal))) +
guides(colour = guide_legend(override.aes = list(size=5))) +
theme(
plot.title = element_text(size = 20, face = "bold")) +
theme(legend.position = "bottom")+ labs(colour="Clade", size="Total Necropsies")+
annotate("text", x=.9, y=50.2, label = "26", size = 7)
ggsave(filename='S26bmrmalmam.pdf', width=13, height=10, limitsize=FALSE,bg="white")
#wxl models #weight and longevity
#wxl neo
cutData <- Data[,c(5,9,10,11,13,40,38,42),drop=FALSE]
cutData[cutData < 0] <-NA
cutData <- na.omit(cutData)
tree <- read.tree("min20Fixed516.nwk")
view(cutData)
cutData$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$Species)[rownames(cutData)]
view(cutData)
wxl.neo<-pglsSEyPagel(NeoplasiaPrevalence~log10(max_longevity.months.*adult_weight.g.),data=cutData,
tree=pruned.tree,method="ML",se=SE)
summary(wxl.neo)
#grab r squared, p value, and lambda from summary
r.v.wxneo <- R2(phy = pruned.tree,wxl.neo)
r.v.wxneo <- format(r.v.wxneo[3])
r.v.wxneo <-signif(as.numeric(r.v.wxneo), digits= 2)
ld.v.wxneo<- summary(wxl.neo)$modelStruct$corStruct
ld.v.wxneo <- signif(ld.v.wxneo[1], digits = 2)
p.v.wxneo<-summary(wxl.neo)$tTable
p.v.wxneo<-signif(p.v.wxneo[2,4], digits = 3)
#plot
ggplot(cutData, aes(y=NeoplasiaPrevalence*100, x=log10(adult_weight.g.)*log10(max_longevity.months.))) +
scale_color_manual(values = c("Mammalia" = "#631879FF"))+
scale_y_continuous(
breaks = c(0, 25,50,75),
labels = c(0, 25,50,75))+
coord_cartesian(xlim = c((min(log10(cutData$adult_weight.g.)*log10(cutData$max_longevity.months.))),max(log10(cutData$adult_weight.g.)*log10(cutData$max_longevity.months.))),
ylim = c(0,75),clip = "off")+
geom_abline(intercept = coef(wxl.neo)[1]*100, slope = coef(wxl.neo)[2]*100,
color = 'grey',size = 1.2) +
theme_cowplot(12)+
theme(axis.title = element_text(size = 18))+
ylab("Neoplasia Prevalence (%)") +
xlab("(log10) Adult Weight(g)*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")+
labs(title = "Neoplasia Prevalence vs. Max Longevity*Weight in Mammals",
subtitle =bquote(p-value:.(p.v.wxneo)~R^2:.(r.v.wxneo)~Lambda:.(ld.v.wxneo))) +
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")+
annotate("text", x=.18, y=83.8, label = "27", size = 7)
ggsave(filename='S27wgtlongneomam.pdf', width=13, height=10, limitsize=FALSE,bg="white")
#wxl mal
cutData <- Data[,c(5,9,10,11,17,40,38,42),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)
wxl.mal<-pglsSEyPagel(MalignancyPrevalence~log10(max_longevity.months.*adult_weight.g.),data=cutData,
tree=pruned.tree,method="ML",se=SE)
summary(wxl.mal)
#grab r squared, p value, and lambda from summary
r.v.wxmal <- R2(phy = pruned.tree,wxl.mal)
r.v.wxmal <- format(r.v.wxmal[3])
r.v.wxmal <-signif(as.numeric(r.v.wxmal), digits= 2)
ld.v.wxmal<- summary(wxl.mal)$modelStruct$corStruct
ld.v.wxmal <- signif(ld.v.wxmal[1], digits = 2)
p.v.wxmal<-summary(wxl.mal)$tTable
p.v.wxmal<-signif(p.v.wxmal[2,4], digits = 3)
#plot
ggplot(cutData, aes(y=MalignancyPrevalence*100, x=log10(adult_weight.g.)*log10(max_longevity.months.))) +
scale_color_manual(values = c("Mammalia" = "#631879FF"))+
scale_y_continuous(
breaks = c(0, 25,45),
labels = c(0, 25,45))+
coord_cartesian(xlim = c((min(log10(cutData$adult_weight.g.)*log10(cutData$max_longevity.months.))),max(log10(cutData$adult_weight.g.)*log10(cutData$max_longevity.months.))),
ylim = c(0,45),clip = "off")+
geom_abline(intercept = coef(wxl.mal)[1]*100, slope = coef(wxl.mal)[2]*100,
color = 'grey',size = 1.2) +
theme_cowplot(12)+
theme(axis.title = element_text(size = 18))+
ylab("Malignancy Prevalence (%)") +
xlab("(log10) Adult Weight(g)*Longevity(Mo)") +
geom_point(aes(colour= Clade, size = RecordsWithDenominators)) +
scale_size(name = "Total Necropsies",
breaks = c(20,100,200,300,477),
labels = c(20,100,200,300,477))+
geom_text_repel(aes(label=ifelse(MalignancyPrevalence > .3,as.character(common_name),'')),max.overlaps = Inf,size=5, direction = "y")+
labs(title = "Malignancy Prevalence vs. Max Longevity*Weight in Mammals",
subtitle =bquote(p-value:.(p.v.wxmal)~R^2:.(r.v.wxmal)~Lambda:.(ld.v.wxmal))) +
guides(colour = guide_legend(override.aes = list(size=5))) +
theme(
plot.title = element_text(size = 20, face = "bold")) +
theme(legend.position = "bottom")+ labs(colour="Clade", size="Total Necropsies")+
annotate("text", x=.18, y=50.2, label = "28", size = 7)
ggsave(filename='S28wxlmal.pdf', width=13, height=10, limitsize=FALSE,bg="white")
#litter size per year neo
cutData <- Data[,c(5,9,10,11,13,34,42),drop=FALSE]
cutData[cutData < 0] <-NA
cutData <- na.omit(cutData)
tree <- read.tree("min20Fixed516.nwk")
cutData$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$Species)[rownames(cutData)]
view(cutData)
lityear.neo<-pglsSEyPagel(NeoplasiaPrevalence~log10(litters_year),data=cutData,
tree=pruned.tree,method="ML",se=SE)
summary(lityear.neo)
#grab r squared, p value, and lambda from summary
r.v.lyearneo <- R2(phy = pruned.tree,lityear.neo)
r.v.lyearneo <- format(r.v.lyearneo[3])
r.v.lyearneo<-signif(as.numeric(r.v.lyearneo), digits= 2)
ld.v.lyearneo<- summary(lityear.neo)$modelStruct$corStruct
ld.v.lyearneo <- signif(ld.v.lyearneo[1], digits = 2)
p.v.lyearneo<-summary(lityear.neo)$tTable
p.v.lyearneo<-signif(p.v.lyearneo[2,4], digits = 3)
#plot
ggplot(cutData, aes(y=NeoplasiaPrevalence*100, x=log10(litters_year))) +
scale_color_manual(values = c("Mammalia" = "#631879FF"))+
scale_y_continuous(
breaks = c(0, 25,50,75),
labels = c(0, 25,50,75))+
geom_abline(intercept = coef(lityear.neo)[1]*100, slope = coef(lityear.neo)[2]*100,
color = 'grey',size = 1.2) +
theme_cowplot(12)+
theme(axis.title = element_text(size = 18))+
ylab("Neoplasia Prevalence (%)") +
xlab("(log10) Litters per Year") +
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")+
labs(title = "Neoplasia Prevalence vs. Litters Per Year in Mammals",
subtitle =bquote(p-value:.(p.v.lyearneo)~R^2:.(r.v.lyearneo)~Lambda:.(ld.v.lyearneo))) +
guides(colour = guide_legend(override.aes=list(size=5),order = 1) ,size= guide_legend(order=2)) +
theme(
plot.title = element_text(size = 20, face = "bold")) +
theme(legend.position = "bottom")+ labs(size="Total Necropsies",colour="Clade")+
coord_cartesian(xlim = c(log10(min(cutData$litters_year)),log10(max(cutData$litters_year))),
ylim = c(0,75),clip = "off")+
annotate("text", x=-.8, y=83.8, label = "29", size = 7)
ggsave(filename='S29lityearneomam.pdf', width=13, height=10, limitsize=FALSE,bg="white")
#litter size mal
cutData <- Data[,c(5,9,10,11,17,34,42),drop=FALSE]
cutData[cutData < 0] <-NA
cutData <- na.omit(cutData)
tree <- read.tree("min20Fixed516.nwk")
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)
lityear.mal<-pglsSEyPagel(MalignancyPrevalence~log10(litters_year),data=cutData,
tree=pruned.tree,method="ML",se=SE)
summary(lityear.mal)
#grab r squared, p value, and lambda from summary
r.v.lyearmal <- R2(phy = pruned.tree,lityear.mal)
r.v.lyearmal <- format(r.v.lyearmal[3])
r.v.lyearmal<-signif(as.numeric(r.v.lyearmal), digits= 2)
ld.v.lyearmal<- summary(lityear.mal)$modelStruct$corStruct
ld.v.lyearmal<- signif(ld.v.lyearmal[1], digits = 2)
p.v.lyearmal<-summary(lityear.mal)$tTable
p.v.lyearmal<-signif(p.v.lyearmal[2,4], digits = 3)
#plot
ggplot(cutData, aes(y=MalignancyPrevalence*100, x=log10(litters_year))) +
scale_color_manual(values = c("Mammalia" = "#631879FF"))+
scale_y_continuous(
breaks = c(0, 25,45),
labels = c(0, 25,45))+
coord_cartesian(xlim = c(log10(min(cutData$litters_year)),log10(max(cutData$litters_year))),
ylim = c(0,45),clip = "off")+
geom_abline(intercept = coef(lityear.mal)[1]*100, slope = coef(lityear.mal)[2]*100,
color = 'grey',size = 1.2) +
theme_cowplot(12)+
theme(axis.title = element_text(size = 18))+
ylab("Malignancy Prevalence (%)") +
xlab("(log10) Litters per Year") +
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(MalignancyPrevalence > .3,as.character(common_name),'')),max.overlaps = Inf,size=5, direction = "y")+
labs(title = "Malignancy Prevalence vs. Litters per Year in Mammals",
subtitle =bquote(p-value:.(p.v.lyearmal)~R^2:.(r.v.lyearmal)~Lambda:.(ld.v.lyearmal))) +
guides(colour = guide_legend(override.aes = list(size=5))) +
theme(
plot.title = element_text(size = 20, face = "bold")) +