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NetworkAnalysis_multimod.R
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NetworkAnalysis_multimod.R
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#####
# Network Analysis for Magel & Francis - this script works through and compares alternative model structures based on new and redevelopment hypoths
# Cross-IS ecosystem model
#QPress package is based on the analysis from Melbourne-Thomas et al 2012
#https://wiley.figshare.com/articles/dataset/Supplement_1_Example_R_code_and_models_/3568107/1
#https://esajournals.onlinelibrary.wiley.com/doi/full/10.1890/12-0207.1?casa_token=VHTR6o_DvQ8AAAAA%3A_vmeRY9Q0b_fiUK3YpRBnKSq5_q-armBNM4ik05kN15SkrRXWQL-uUfmJyENGHCm_gN0-VY8rh-amw
#Some modifications were made to the original QPress code - they are contained in QPressFunctions.R
# Load required packages
library(QPress)
library(tcltk2)
library(XML)
library(here) #folder management
##### Load the Dia models #####
statquo <- QPress::model.dia("./DiaModels/MultiMods_Jan2022/StatusQuo_Jan2022_forR.dia")
moderate <- QPress::model.dia("./DiaModels/MultiMods_Jan2022/Moderate_Jan2022_forR.dia")
newdev_A <- QPress::model.dia("./DiaModels/MultiMods_Jan2022/NewDevA_Jan2022_forR.dia")
newdev_B <- QPress::model.dia("./DiaModels/MultiMods_Jan2022/NewDevB_Jan2022_forR.dia")
newdev_C <- QPress::model.dia("./DiaModels/MultiMods_Jan2022/NewDevC_Jan2022_forR.dia")
newdev_D <- QPress::model.dia("./DiaModels/MultiMods_Jan2022/NewDevD_Jan2022_forR.dia")
redev_E <- QPress::model.dia("./DiaModels/MultiMods_Jan2022/RedevE_Jan2022_forR.dia")
redev_F <- QPress::model.dia("./DiaModels/MultiMods_Jan2022/RedevF_Jan2022_forR.dia")
redev_G <- QPress::model.dia("./DiaModels/MultiMods_Jan2022/RedevG_Jan2022_forR.dia")
redev_H <- QPress::model.dia("./DiaModels/MultiMods_Jan2022/RedevH_Jan2022_forR.dia")
statquo_harvey <- QPress::model.dia("./DiaModels/StatusQuo_Jul2022_forR_HarveyTrial.dia")
moderate_harvey <- QPress::model.dia("./DiaModels/Moderate_Jul2022_forR_HarveyTrial.dia")
## Examine unweighted adjacency matrices
A_Statquo <- adjacency.matrix(statquo)
A_Statquo
write.csv(A_Statquo, file = "StatusQuo_AdjMatrix.csv", row.names = FALSE) #save the matrix, ifn needed
## Enforce limitation (This is redundant with the self-limiting edges in the Dia model, if they are already included)
statquo <- QPress::enforce.limitation(statquo)
moderate <- QPress::enforce.limitation(moderate)
newdev_A <- QPress::enforce.limitation(newdev_A)
newdev_B <- QPress::enforce.limitation(newdev_B)
newdev_C <- QPress::enforce.limitation(newdev_C)
newdev_D <- QPress::enforce.limitation(newdev_D)
redev_E <- QPress::enforce.limitation(redev_E)
redev_F <- QPress::enforce.limitation(redev_F)
redev_G <- QPress::enforce.limitation(redev_G)
redev_H <- QPress::enforce.limitation(redev_H)
statquo_harvey <- QPress::enforce.limitation(statquo_harvey)
moderate_harvey <- QPress::enforce.limitation(moderate_harvey)
##### Simulations #####
#If model simulations already exist, load them
sims_statquo <- readRDS("Sims_StatusQuo_10000_2022-02-09.rds")
sims_moderate <- readRDS("Sims_Moderate_10000_2022-02-09.rds")
sims_newdev_A <- readRDS("Sims_newdev_A_10000_2022-02-09.rds")
sims_newdev_B <- readRDS("Sims_newdev_B_10000_2022-02-09.rds")
sims_newdev_C <- readRDS("Sims_newdev_C_10000_2022-02-09.rds")
sims_newdev_D <- readRDS("Sims_newdev_D_10000_2022-02-09.rds")
sims_redev_E <- readRDS("Sims_redev_E_10000_2022-02-09.rds")
sims_redev_F <- readRDS("Sims_redev_F_10000_2022-02-09.rds")
sims_redev_G <- readRDS("Sims_redev_G_10000_2022-02-09.rds")
sims_redev_H <- readRDS("Sims_redev_H_10000_2022-02-09.rds")
#If model simulation does not exist, simulate and save!
n_sims <- 10000 #number of accepted simulations requested
sims_statquo <- QPress::system.simulate(n_sims, statquo)
sims_statquo$total #66346
sims_moderate <- QPress::system.simulate(n_sims, moderate)
sims_moderate$total #66666
sims_newdev_A <- QPress::system.simulate(n_sims, newdev_A)
sims_newdev_A$total #65507
sims_newdev_B <- QPress::system.simulate(n_sims, newdev_B)
sims_newdev_B$total #66231
sims_newdev_C <- QPress::system.simulate(n_sims, newdev_C)
sims_newdev_C$total #65372
sims_newdev_D <- QPress::system.simulate(n_sims, newdev_D)
sims_newdev_D$total #32904
sims_redev_E <- QPress::system.simulate(n_sims, redev_E)
sims_redev_E$total #66698
sims_redev_F <- QPress::system.simulate(n_sims, redev_F)
sims_redev_F$total #66860
sims_redev_G <- QPress::system.simulate(n_sims, redev_G)
sims_redev_G$total #65907
sims_redev_H <- QPress::system.simulate(n_sims, redev_H)
sims_redev_H$total #65929
sims_statquo_harvey <- QPress::system.simulate(n_sims, statquo_harvey)
sims_statquo_harvey$total #104423
sims_moderate_harvey <- QPress::system.simulate(n_sims, moderate_harvey)
sims_moderate_harvey$total #102063
##### Save Simulations #####
saveRDS(sims_statquo, file = paste("Sims_", "StatusQuo_", n_sims, "_", Sys.Date(), ".rds", sep = ""))
saveRDS(sims_moderate, file = paste("Sims_", "Moderate_", n_sims, "_", Sys.Date(), ".rds", sep = ""))
saveRDS(sims_newdev_A, file = paste("Sims_", "newdev_A_", n_sims, "_", Sys.Date(), ".rds", sep = ""))
saveRDS(sims_newdev_B, file = paste("Sims_", "newdev_B_", n_sims, "_", Sys.Date(), ".rds", sep = ""))
saveRDS(sims_newdev_C, file = paste("Sims_", "newdev_C_", n_sims, "_", Sys.Date(), ".rds", sep = ""))
saveRDS(sims_newdev_D, file = paste("Sims_", "newdev_D_", n_sims, "_", Sys.Date(), ".rds", sep = ""))
saveRDS(sims_redev_E, file = paste("Sims_", "redev_E_", n_sims, "_", Sys.Date(), ".rds", sep = ""))
saveRDS(sims_redev_F, file = paste("Sims_", "redev_F_", n_sims, "_", Sys.Date(), ".rds", sep = ""))
saveRDS(sims_redev_G, file = paste("Sims_", "redev_G_", n_sims, "_", Sys.Date(), ".rds", sep = ""))
saveRDS(sims_redev_H, file = paste("Sims_", "redev_H_", n_sims, "_", Sys.Date(), ".rds", sep = ""))
saveRDS(sims_statquo_harvey, file = paste("Sims_", "statquo_harvey_", n_sims, "_", Sys.Date(), ".rds", sep = ""))
saveRDS(sims_moderate_harvey, file = paste("Sims_", "moderate_harvey_", n_sims, "_", Sys.Date(), ".rds", sep = ""))
##### Perturbations #####
#using custom exploratory widget & print results from QPressFunctions.R
#Save the pos/neg sims by copying/pasting last output into Excel
# Status Quo: urban vs. rural
impact.barplot(sim = sims_statquo) # (+ New Development, - Redevelopment)
impact.barplot(sim = sims_statquo) # (+ Redevelopment, - New Development)
# Rural Strategies only (+ New Development)
impact.barplot(sim = sims_statquo)
impact.barplot(sim = sims_newdev_A)
impact.barplot(sim = sims_newdev_B)
impact.barplot(sim = sims_newdev_C)
impact.barplot(sim = sims_newdev_D)
# Urban Strategies only (+ Redevelopment)
impact.barplot(sim = sims_statquo)
impact.barplot(sim = sims_redev_E)
impact.barplot(sim = sims_redev_F)
impact.barplot(sim = sims_redev_G)
impact.barplot(sim = sims_redev_H)
# Coordinated Urban + rural (+ New Development, + Redevelopment)
impact.barplot(sim = sims_statquo)
impact.barplot(sim = sims_moderate)
impact.barplot(sim = sims_statquo_harvey)
impact.barplot(sim = sims_moderate_harvey)
#Examine weight values in the accepted model runs:
sims_statquo$edges
mean(abs(sims_statquo$w)) # 0.5004479
sims_moderate$edges
mean(abs(sims_moderate$w)) # 0.5005908
mean(abs(sims_newdev_A$w)) # 0.5006354
mean(abs(sims_newdev_B$w)) # 0.500851
mean(abs(sims_newdev_C$w)) # 0.5007896
mean(abs(sims_newdev_D$w)) # 0.5005958
mean(abs(sims_redev_E$w)) # 0.5007229
mean(abs(sims_redev_F$w)) # 0.5006543
mean(abs(sims_redev_G$w)) # 0.5005669
mean(abs(sims_redev_H$w)) # 0.5004286
##################################################################################################
##################################################################################################
# For generating plots that are not part of the QPress package, use code below
# Example code was provided by Ben Raymond, but this came from K. Sobocinski
# Extract the bits we need from the Status Quo Model
edges <- sims_statquo$edges
write.csv(edges, file = "StatusQuo_EdgesList.csv")
As <- sims_statquo$A
nodes <- node.labels(edges)
write.csv(nodes, file = "StatusQuo_NodesList.csv")
monitor <- c(rep(NA,length(nodes))) ## Don't enforce any required responses
#Call specific nodes of interest
#To show only a subset of node responses (e.g. 12, 13, 27, 4, 31), run this instead of standard plot:
myplot <- function(nodes,As,perturb,monitor,epsilon=1.0E-5,main="",cex.axis=1) {
pal <- c("grey30", "gray80", "tomato2")
results <- matrix(0,length(nodes),3)
for(i in 1:length(As)) {
impact <- signum(drop(As[[i]]%*%perturb),epsilon=epsilon)
if(all(monitor==impact,na.rm=T)) {
results <- results + outer(impact,-1:1,'==')
}
}
rownames(results) <- nodes
nodes <- nodes[c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33)] #this is where you specify the nodes of interest
results <- results[c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33),] #this is where you specify the nodes of interest
lwidth <- max(strwidth(nodes,units="inches",cex=cex.axis))
opar <- par(mai=c(0.5,lwidth+0.15,0.15,0.15)+0.2)
barplot(t(results),horiz=T,las=1,border=F,col=pal,
xlab="Simulations",main=main,cex.axis=cex.axis)
par(opar)
}
# Standard slider plot of all response nodes, perturbing each in turn given the vector of perturbations (press):
# windows()
# To output to PDF
currentDate <- Sys.Date()
Indiv_Perturb <- paste(currentDate,"_PerturbationPlots_pos",".pdf",sep="")
pdf(file = Indiv_Perturb)
# For function
#opar <- par
#par(mfrow=c(2,2)) #This can be invoked for a 2x2 layout (better for simple (reduced vars) plot)
for (i in 1:33) { #number of nodes in model
#i=2
#Set up desired directions of perturbations--based upon direction of press (-1,1)
#For all presses (should have 1 per node)
press = c(1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1)
#length(press)
presses=diag(press, nrow=33, ncol=33)
perturb=presses[i,]
perturb2=ifelse(perturb==1,"(Increase)","(Decrease)")
#If all press perturbs are positive, use this code:
#perturb <- c(rep(0,length(nodes)))
#perturb[i] <- 1 ## perturb the ith node, this is a positive perturbation
#Choose: all nodes (impact.barplot.action) or a subset of nodes (myplot)--myplot code below
#impact.barplot.action(nodes,As,perturb,monitor,main=c(nodes[i],perturb[i]))
myplot(nodes,As,perturb,monitor,main=c(nodes[i],perturb2[i]))
}
par(opar)
dev.off()
#NEGATIVES
Indiv_Perturb <- paste(currentDate,"_PerturbationPlots_neg",".pdf",sep="")
pdf(file = Indiv_Perturb)
# For function
#opar <- par
#par(mfrow=c(2,2)) #This can be invoked for a 2x2 layout (better for simple (reduced vars) plot)
for (i in 1:33) { #number of nodes in model
#i=2
#Set up desired directions of perturbations--based upon direction of press (-1,1)
#For all presses (should have 1 per node)
press = c(-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1)
#length(press)
presses=diag(press, nrow=33, ncol=33)
perturb=presses[i,]
perturb2=ifelse(perturb==1,"(Increase)","(Decrease)")
#If all press perturbs are positive, use this code:
#perturb <- c(rep(0,length(nodes)))
#perturb[i] <- 1 ## perturb the ith node, this is a positive perturbation
#Choose: all nodes (impact.barplot.action) or a subset of nodes (myplot)--myplot code below
#impact.barplot.action(nodes,As,perturb,monitor,main=c(nodes[i],perturb[i]))
myplot(nodes,As,perturb,monitor,main=c(nodes[i],perturb2[i]))
}
par(opar)
dev.off()
##### Edge Weight "Sensitivity" Analysis #####
#Code adapted from K. Sobocinski
library(reshape2)
library(ggplot2)
#write a function to plot the outlier edge weights for a given simulation
plot_outwts = function (sims)
{
print("plotting outlier weights")
weight <- as.data.frame(sims$w) #extract the weight values in the accepted model runs
wts <- reshape2::melt(weight)
colnames(wts)=c("Edge", "Value")
edgemean <- as.data.frame(apply(sims$w, 2, mean)) #calculate weight mean for each edge
edgestdev <- as.data.frame(apply(sims$w, 2, sd)) #calculate weight stdev for each edge
lowerSD <- abs(edgemean[,1])-abs(edgestdev[,1])
upperSD <- abs(edgemean[,1])+abs(edgestdev[,1])
edgenames <- as.data.frame(levels(wts$Edge))
edgesSD <- cbind(edgenames, abs(edgemean[,1]), lowerSD, upperSD)
colnames(edgesSD)=c("Edge", "Mean", "LowerSD", "UpperSD")
#Pull out outliers (top/bottom 15 values) & reorder EdgesSD by mean
bottom <- edgesSD %>% dplyr::slice_min(Mean, n = 15)
top <- edgesSD %>% dplyr::slice_max(Mean, n = 15)
outliers <- rbind(top, bottom)
#Plot all outliers (top/bottom 15 values)
ggplot(outliers, aes(x=Mean, y=reorder(Edge, Mean))) +
geom_errorbarh(data=outliers, aes(xmax=UpperSD, xmin=LowerSD), colour = "grey50") +
ylab("Edge") +
xlab("Weight (mean +/- SD)") +
theme_bw() +
geom_point() +
geom_vline(xintercept=0, linetype="dotted") +
geom_vline(xintercept=1.0, linetype="dotted") +
geom_vline(xintercept=0.5, linetype="dotted") + theme(axis.text=element_text(size=12),
axis.title=element_text(size=12,face="bold"))
}
plot_outwts(sims = sims_statquo)
plot_outwts(sims = sims_moderate)
plot_outwts(sims = sims_newdev_A)
plot_outwts(sims = sims_newdev_B)
plot_outwts(sims = sims_newdev_C)
plot_outwts(sims = sims_newdev_D)
plot_outwts(sims = sims_redev_E)
plot_outwts(sims = sims_redev_F)
plot_outwts(sims = sims_redev_G)
plot_outwts(sims = sims_redev_H)
##### Old sensitivity analysis code #####
#To extract the weight values in the accepted model runs:
sims_statquo$edges
head(sims_statquo$w)
tail(sims_statquo$w)
is.matrix(sims_statquo$w) #True
weight_statquo <- as.data.frame(sims_statquo$w)
head(weight_statquo)
#Check distributions of different nodes to see how variable they are
hist(weight_statquo[,10])
#To assess the sensitivity of the weights
#Extract edges and weights from simulations
wts_statquo <- reshape2::melt(weight_statquo)
colnames(wts_statquo)=c("Edge", "Value")
head(wts_statquo)
dim(sims_statquo$w) #10000 x 143 (143 linkages in the model)
#Get means for each edge
edgemean <- as.data.frame(apply(sims_statquo$w, 2, mean))
summary(edgemean)
edgemin <- as.data.frame(apply(sims_statquo$w, 2, min))
edgemax <- as.data.frame(apply(sims_statquo$w, 2, max))
edgestdev <- as.data.frame(apply(sims_statquo$w, 2, sd))
hist(abs(edgemean[,1]))
hist(abs(edgemin[,1]))
hist(abs(edgemax[,1]))
hist(abs(edgestdev[,1]))
lowerSD <- abs(edgemean[,1])-abs(edgestdev[,1])
upperSD <- abs(edgemean[,1])+abs(edgestdev[,1])
edgenames <- as.data.frame(levels(wts_statquo$Edge))
edge.vals <- cbind(edgenames, abs(edgemean[,1]), abs(edgemin[,1]),
abs(edgemax[,1]))
#USe max and min values with mean
head(edge.vals)
dim(edge.vals) #143 linkages in the model x 4 columns
colnames(edge.vals)=c("Edge", "Mean", "Min", "Max")
str(edge.vals)
#Use SD
edgesSD <- cbind(edgenames, abs(edgemean[,1]), lowerSD, upperSD)
head(edgesSD)
colnames(edgesSD)=c("Edge", "Mean", "LowerSD", "UpperSD")
#Plot all edges and means, maxes, and mins
ggplot(edge.vals, aes(x=Mean, y=Edge)) +
geom_errorbarh(data=edge.vals, aes(xmax=Max, xmin=Min), colour = "grey50") +
geom_point()
#Reorder so easier to see
ggplot(edgesSD, aes(x=Mean, y=reorder(Edge, Mean))) +
geom_errorbarh(data=edgesSD, aes(xmax=UpperSD, xmin=LowerSD), colour = "grey50") +
geom_point() + theme_bw() + xlab("Weight abs(mean +/- sd)") + ylab("Edge") +
geom_vline(xintercept=0, linetype="dotted") +
geom_vline(xintercept=1.0, linetype="dotted") +
geom_vline(xintercept=0.5, linetype="dotted") + theme(axis.text=element_text(size=6),
axis.title=element_text(size=10,face="bold"))
#Pull out outliers (top/bottom 15 values)
#Reorder EdgesSD by mean
bottom <- edgesSD %>% dplyr::slice_min(Mean, n = 15)
top <- edgesSD %>% dplyr::slice_max(Mean, n = 15)
outliers <- rbind(top, bottom)
#Plot all outliers (top/bottom 15 values)
ggplot(outliers, aes(x=Mean, y=reorder(Edge, Mean))) +
geom_errorbarh(data=outliers, aes(xmax=UpperSD, xmin=LowerSD), colour = "grey50") +
ylab("Edge") +
xlab("Weight (mean +/- sd)") +
theme_bw() +
geom_point() +
geom_vline(xintercept=0, linetype="dotted") +
geom_vline(xintercept=1.0, linetype="dotted") +
geom_vline(xintercept=0.5, linetype="dotted") + theme(axis.text=element_text(size=12),
axis.title=element_text(size=12,face="bold"))
#To plot bottom (most sensitive edges only):
ggplot(bottom, aes(x=Mean, y=reorder(Edge, Mean))) +
geom_errorbarh(data=bottom, aes(xmax=UpperSD, xmin=LowerSD), colour = "grey50") +
ylab("Edge") +
xlab("Mean Weight") +
theme_bw() +
theme(axis.text=element_text(size=10),
axis.title=element_text(size=10,face="bold"))+
geom_point() +
geom_vline(xintercept=0.5, linetype="dotted")