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# FILENAME: "hump.results.R"
# AUTHOR: Dan McGlinn
# EMAIL: danmcglinn@gmail.com
# DATE CREATED: 11.15.2009
# LAST MODIFIED: 12.30.2009
# PURPOSE: To create an output file from the simulation model
# and to examine it graphically
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##set the working directory (i.e, location of the file 'hump.sim.R')
setwd(".../hump simualtion")

##parameterization as listed in Table 1.
##set inital diameter size
diams = .05
##set number of species in species pool
numsp = 100
##set maximum amount of time to run simulation for
Tim = 50
##set the growth increment
diamch = 0.01
##set the inital number of individuals
ns<-c(5,7,9,11,13,15)^2 

##import all the functions in the file 'hump.sim.R'
source('hump.sim.R') 
##alternatively this can be done by copying and pasting all of the code in this file into the R console

##if using many permuations then it may be necessary to increase
##the memory limit that is typically reserved for a 32-bit R install
memory.limit(size=4095)
system.time(output<-hump.sim(200,diams,ns,RAD='even',space=c('clustered','random','uniform'),intensity=.95))
##takes approximatly 1 hour on a 64 bit machine,w/Intel core i7cpu 940@2.93 GHz 
##with 8 GB of RAM on R 2.9.2
##may wish to save the output file now that it is generated
##save.image("simoutput.RData")




##generate graphical output observed in manuscript
##FIGURE 1 - top-down view of simulated communities 
install.packages('dichromat') ##this package is necessary to use the color scheme called below
library(dichromat)
cls<- colorRampPalette(colorschemes$Categorical.12)(numsp) 
axisfnt<-1.25
par(mfrow=c(3,3))
tims<-c(1,15,30)
iperm<-4 ##chose a permuation to examine
##place the names of the spatial distributions considered,this will be used for the main title of the graphic 
spaces<-c('Clustered','Random','Uniform')
mult.diam<-50*par()$cex ##necessary to scale point size
##plot the points
for(t in 1:length(tims)){
 for(s in 1:3){
  par(mar=c(2.5,2.5,2.5,2.5))
  ifelse(t==1,main<-paste(spaces[s]),main<-'')
  plot(output[iperm,s,1,length(ns),tims[t],1:ns[length(ns)],1:2],main=main,xlab='',ylab='',xlim=c(0,1),ylim=c(0,1),col=cls[output[iperm,s,1,length(ns),tims[t],1:ns[length(ns)],3]],pch=19,cex=mult.diam*output[iperm,s,1,length(ns),tims[t],1:ns[length(ns)],4],axes=F,frame.plot=T,lwd=2)
  axis(side=1,at=c(0,.5,1),cex.axis=axisfnt)
  axis(side=2,at=c(0,.5,1),cex.axis=axisfnt)
}}

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##FIGURE 2 - Three bivariate relationships generated by the simulation model
greens<-c('#99CC99','#669966')
blues<-c('#336699','#99CCFF')
purples<-c('#660066','#996699')

par(mfrow=c(3,3))
lwd<-2
pchs<-1
## number of individuas vs time
time.plot(output,type='l',avg=T,ci=T,poly=T,cls=c(purples[1],NA,NA),cls.poly=c(purples[2],NA,NA),lwd=lwd,pch=pchs,main='Clustered')
time.plot(output,type='l',avg=T,ci=T,poly=T,cls=c(NA,blues[1],NA),cls.poly=c(NA,blues[2],NA),lwd=lwd,pch=pchs,main='Random')
time.plot(output,type='l',avg=T,ci=T,poly=T,cls=c(NA,NA,greens[2]),cls.poly=c(NA,NA,greens[1]),lwd=lwd,pch=pchs,main='Uniform')

## number of species vs number of individuals 
raref.plot(output,type='l',avg=T,ci=T,poly=T,cls=c(purples[1],NA,NA),cls.poly=c(purples[2],NA,NA),lwd=lwd,pch=pchs)
raref.plot(output,type='l',avg=T,ci=T,poly=T,cls=c(NA,blues[1],NA),cls.poly=c(NA,blues[2],NA),lwd=lwd,pch=pchs)
raref.plot(output,type='l',avg=T,ci=T,poly=T,cls=c(NA,NA,greens[2]),cls.poly=c(NA,NA,greens[1]),lwd=lwd,pch=pchs)

## number of species vs total biomass
hump.plot(output,type='l',avg=T,ci=T,poly=T,cls=c(purples[1],NA,NA),cls.poly=c(purples[2],NA,NA),lwd=lwd,pch=pchs)
hump.plot(output,type='l',avg=T,ci=T,poly=T,cls=c(NA,blues[1],NA),cls.poly=c(NA,blues[2],NA),lwd=lwd,pch=pchs)
hump.plot(output,type='l',avg=T,ci=T,poly=T,cls=c(NA,NA,greens[2]),cls.poly=c(NA,NA,greens[1]),lwd=lwd,pch=pchs)

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##FIGURE 3 - Unaveraged results
par(mfrow=c(1,3))
lwd<-1
pchs<-1
#par(cex=par()$cex*.5)
#par(cex.axis=1/.5)
cls<-c(purples[2],blues[2],greens[1])
hump.plot(output,type='p',avg=F,cls=c(cls[1],NA,NA),lwd=lwd,pch=pchs,main='Clustered')
hump.plot(output,type='p',avg=F,cls=c(NA,cls[2],NA),lwd=lwd,pch=pchs,main='Random')
hump.plot(output,type='p',avg=F,cls=c(NA,NA,cls[3]),lwd=lwd,pch=pchs,main='Uniform')