### Loading the Marginal Likelihood values
ss_marginal_muhisse <- -17558.79 # From the file MuHiSSE_ssMarginal
ss_marginal_cid <- -17563.46 # From the file CID_ssMarginal
ss_marginal_musse <- -17648.86 # From the file MuSSE_ssMarginal
BF_muhisseXmusse <- ss_marginal_muhisse - ss_marginal_musse #90.07 decisive support for muhisse
BF_musseXcid <- ss_marginal_cid - ss_marginal_musse         #85.4 decisive support for cid
BF_muhisseXcid <- ss_marginal_muhisse - ss_marginal_cid     #4.67 decisive support for muhisse
BF_muhisseXmusse
BF_musseXcid
BF_muhisseXcid
### Loading the Marginal Likelihood values
ss_marginal_muhisse <- -17092.75 # From the file MuHiSSE_ssMarginal
ss_marginal_cid <- -17098.48 # From the file CID_ssMarginal
ss_marginal_musse <- -17215.47 # From the file MuSSE_ssMarginal
BF_muhisseXmusse <- ss_marginal_muhisse - ss_marginal_musse #90.07 decisive support for muhisse
BF_musseXcid <- ss_marginal_cid - ss_marginal_musse         #85.4 decisive support for cid
BF_muhisseXcid <- ss_marginal_muhisse - ss_marginal_cid     #4.67 decisive support for muhisse
BF_muhisseXmusse
BF_musseXcid
BF_muhisseXcid
### Loading the Marginal Likelihood values
ss_marginal_muhisse <- -17099.31 # From the file MuHiSSE_ssMarginal
ss_marginal_cid <- -17092.39 # From the file CID_ssMarginal
ss_marginal_musse <- -17216.27 # From the file MuSSE_ssMarginal
BF_muhisseXmusse <- ss_marginal_muhisse - ss_marginal_musse #90.07 decisive support for muhisse
BF_musseXcid <- ss_marginal_cid - ss_marginal_musse         #85.4 decisive support for cid
BF_muhisseXcid <- ss_marginal_muhisse - ss_marginal_cid     #4.67 decisive support for muhisse
BF_muhisseXmusse
BF_musseXcid
BF_muhisseXcid
library(ape)
library(phytools)
library(plyr)
library(tidyverse)
library(ggplot2)
library(ggthemes)
library(RevGadgets)
#------------- Siqueira et al -------------#
#-- Evolution of fish-coral interactions --#
#------------ RevBayes ModComp-------------#
#----- Setting WD -----#
wd <- "~/Desktop/Ongoing_papers/Coral_association/Revision/Final/submit/Siqueira_etal_Code_and_Data"   #Set your personal working directory here
setwd(wd)
#----- Loading the Marginal Likelihood values -----#
ss_marginal_muhisse <- as.numeric(readLines("RevBayes/MuHiSSE/output/Mod_comp/MuHiSSE_ssMarginal.txt", warn = FALSE)) #-17558.79 - From the file MuHiSSE_ssMarginal
ss_marginal_cid <- as.numeric(readLines("RevBayes/CID/output/Mod_comp/CID_ssMarginal.txt", warn = FALSE))             #-17563.46 - From the file CID_ssMarginal
ss_marginal_musse <- as.numeric(readLines("RevBayes/MuSSE/output/Mod_comp/MuSSE_ssMarginal.txt", warn = FALSE))       #-17648.86 - From the file MuSSE_ssMarginal
### Calculating Bayes factors between models
# BF = MarginalLikelihood_M0 - MarginalLikelihood_M1 - Check table at https://revbayes.github.io/tutorials/model_selection_bayes_factors/bf_intro.html for strengh of evidence
BF_muhisseXmusse <- ss_marginal_muhisse - ss_marginal_musse #90.07 decisive support for muhisse
BF_musseXcid <- ss_marginal_cid - ss_marginal_musse         #85.4 decisive support for cid
BF_muhisseXcid <- ss_marginal_muhisse - ss_marginal_cid     #4.67 decisive support for muhisse
BF_muhisseXmusse
BF_musseXcid
BF_muhisseXcid
#----- Loading packages -----#
library(tidyverse)  #Version 2.0.0
library(scales)     #Version 2.0.0
library(igraph)
packageVersion("tidyverse")
packageVersion("scales")
packageVersion("igraph")
#----- Setting WD -----#
wd <- "~/Desktop/Ongoing_papers/Coral_association/Revision/Final/submit/Siqueira_etal_Code_and_Data"   #Set your personal working directory here
setwd(wd)
#----- Loading data -----#
data <- read.table("RevBayes/MuHiSSE/output/coral_association_MuHiSSE_coral_association.log",header=TRUE)
# Removing 10% burnin
data <- data[2003:nrow(data),]
#----- Plotting diversification rates -----#
# Filtering evolutionary rates from the posterior dataset
data_spe <- data[,grep("speciation.", colnames(data), value = T)]
data_ext <- data[,grep("extinction.", colnames(data), value = T)]
data_netdiv <- data_spe[,1:6] - data_ext[,1:6]
colnames(data_netdiv) <- c("Nonassociated","Moderate","Strong",
"NonassociatedHidden","ModerateHidden","StrongHidden")
## Plotting - Figure 4A
ggplot(data_netdiv, aes(x=value, fill=rate)) +
geom_density(linewidth = 0.3, col = "white") +
scale_x_continuous(limits = c(-0.01, 0.25), expand = c(0,0)) +
scale_fill_manual(breaks=c("Nonassociated","Moderate","Strong",
"NonassociatedHidden","ModerateHidden","StrongHidden"),
values = c("#86BBD8","#1E84AD","#2F4858",
alpha("#86BBD8",0.5),alpha("#1E84AD",0.5),alpha("#2F4858",0.5))) +
scale_y_continuous(expand = c(0.01,0.01)) +
theme_bw() +
ylab("Posterior Density") + xlab("Net Diversification (lineages/Ma)") +
theme(text = element_text(size=12),
legend.title = element_blank(),
legend.position = c(.8,.8),
legend.background = element_rect(fill = "transparent", colour = NA),
panel.background = element_rect(fill = "grey95",
colour = "grey95",
linetype = "solid"),
panel.grid.major = element_line(size = 0.5, linetype = 'solid',
colour = "white"),
panel.grid.minor = element_line(size = 0.25, linetype = 'solid',
colour = "white"))
## Plotting - Figure 4A
ggplot(data_netdiv, aes(x=value, fill=rate)) +
geom_density(size = 0.3, col = "white") +
scale_x_continuous(limits = c(-0.01, 0.25), expand = c(0,0)) +
scale_fill_manual(breaks=c("Nonassociated","Moderate","Strong",
"NonassociatedHidden","ModerateHidden","StrongHidden"),
values = c("#86BBD8","#1E84AD","#2F4858",
alpha("#86BBD8",0.5),alpha("#1E84AD",0.5),alpha("#2F4858",0.5))) +
scale_y_continuous(expand = c(0.01,0.01)) +
theme_bw() +
ylab("Posterior Density") + xlab("Net Diversification (lineages/Ma)") +
theme(text = element_text(size=12),
legend.title = element_blank(),
legend.position = c(.8,.8),
legend.background = element_rect(fill = "transparent", colour = NA),
panel.background = element_rect(fill = "grey95",
colour = "grey95",
linetype = "solid"),
panel.grid.major = element_line(size = 0.5, linetype = 'solid',
colour = "white"),
panel.grid.minor = element_line(size = 0.25, linetype = 'solid',
colour = "white"))
#----- Plotting diversification rates -----#
# Filtering evolutionary rates from the posterior dataset
data_spe <- data[,grep("speciation.", colnames(data), value = T)]
data_ext <- data[,grep("extinction.", colnames(data), value = T)]
data_netdiv <- data_spe[,1:6] - data_ext[,1:6]
colnames(data_netdiv) <- c("Nonassociated","Moderate","Strong",
"NonassociatedHidden","ModerateHidden","StrongHidden")
data_netdiv <- data_netdiv %>%
pivot_longer(cols = Nonassociated:StrongHidden, names_to = "rate", values_to = "value")
## Plotting - Figure 4A
ggplot(data_netdiv, aes(x=value, fill=rate)) +
geom_density(size = 0.3, col = "white") +
scale_x_continuous(limits = c(-0.01, 0.25), expand = c(0,0)) +
scale_fill_manual(breaks=c("Nonassociated","Moderate","Strong",
"NonassociatedHidden","ModerateHidden","StrongHidden"),
values = c("#86BBD8","#1E84AD","#2F4858",
alpha("#86BBD8",0.5),alpha("#1E84AD",0.5),alpha("#2F4858",0.5))) +
scale_y_continuous(expand = c(0.01,0.01)) +
theme_bw() +
ylab("Posterior Density") + xlab("Net Diversification (lineages/Ma)") +
theme(text = element_text(size=12),
legend.title = element_blank(),
legend.position = c(.8,.8),
legend.background = element_rect(fill = "transparent", colour = NA),
panel.background = element_rect(fill = "grey95",
colour = "grey95",
linetype = "solid"),
panel.grid.major = element_line(size = 0.5, linetype = 'solid',
colour = "white"),
panel.grid.minor = element_line(size = 0.25, linetype = 'solid',
colour = "white"))
#----- Plotting transition rates -----#
# Defining colours
cols<-setNames(c("#86BBD8","#1E84AD","#2F4858"),colnames(mat_trans))
#----- Plotting transition rates -----#
# Filtering transition rates from posterior dataset
data_trans <- data[,grep("transition", colnames(data), value = T)]
mat_trans <- matrix(nrow = 3, ncol = 3, dimnames = list(c("Nonassociated","Moderate","Strong"),
c("Nonassociated","Moderate","Strong")))
mat_trans[1,2] <- round(median(data_trans$transition_rates.1.), 5)
mat_trans[1,3] <- round(median(data_trans$transition_rates.2.), 5)
mat_trans[2,1] <- round(median(data_trans$transition_rates.3.), 5)
mat_trans[2,3] <- round(median(data_trans$transition_rates.4.), 5)
mat_trans[3,1] <- round(median(data_trans$transition_rates.5.), 5)
mat_trans[3,2] <- round(median(data_trans$transition_rates.6.), 5)
# Defining colours
cols<-setNames(c("#86BBD8","#1E84AD","#2F4858"),colnames(mat_trans))
#----- Igraph functions -----#
source("~/Desktop/Ongoing_papers/Coral_association/Scripts/Siqueira_etal_functions.R")
environment(plot.igraph2) <- asNamespace('igraph')
environment(igraph.Arrows2) <- asNamespace('igraph')
g2 <- graph_from_adjacency_matrix(mat_trans, weighted=TRUE,diag=F)
#pdf("Figures/Siqueira_etal_Fig4B.pdf", height = 8, width = 8)
plot.igraph2(g2, layout = layout.circle,
vertex.size = 50,
vertex.label.cex = 1.2,
vertex.color = cols,
vertex.frame.color = "white",
vertex.width = 2,
vertex.label.color = "grey99",
vertex.label.family = "Helvetica",
edge.color=c(cols[1],cols[3],cols[2],cols[2],cols[1],cols[3]),
edge.width=(E(g2)$weight/max(E(g2)$weight))*25,
edge.arrow.size=(E(g2)$weight/max(E(g2)$weight))*5,
edge.label.family = "Helvetica",
edge.label.cex = 0.8,
edge.curved = 0.5,
rescale = F, axes = F)
mat_trans
