dciR

THIS VERSION ONLY WORKS ON LINUX DUE TO INCOMPATABILITY BETWEEN RcppOctave AND WINDOWS!

A modification of the directional connectivity index calculation from Larsen et al. (2012). Essentially replaces MatlabBGL with RBGL. For convenience, Octave is called from R using RcppOctave.

Requires RBGL, Octave, and RcppOctave (verified to work with version 3.8.1 and 0.14.5 respectively)

Installation

install.packages('devtools')  # package devtools needed
devtools::install_github('jsta/dciR')

source("http://bioconductor.org/biocLite.R")
biocLite("RBGL")
#biocLite("Rgraphviz")

Examples

#The example below reproduces figure 3 from Larsen et al. (2012)#####
library(dciR)
   
#load test matricies####
ma <- matrix(c(0,0,1,0,0,0,1,0,1,0,0,1,0,0,1,0,0,0,1,0,1,1,0,0,0),nrow=5,byrow=T)
mb <- matrix(c(1,1,1,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,1,1,1,0,0),nrow=5,byrow=T)
mc <- matrix(c(1,0,1,0,1,1,0,1,0,1,1,0,1,0,1,1,0,1,0,1,1,0,1,0,1),nrow=5,byrow=T)
md <- matrix(c(1,0,0,0,1,1,0,1,0,1,0,0,1,0,0,1,0,0,0,1,1,0,0,0,1),nrow=5,byrow=T)

data.frame(cbind(letters[1:4],round(unlist(lapply(list(ma,mb,mc,md),function(x) dci(x,1,1))),3)))

#The example below reproduces Figure 4####
library(jpeg)
library(raster)

c1<-readJPEG(system.file("images","conserved1.jpg",package="dciR"))
c2<-readJPEG(system.file("images","conserved2.jpg",package="dciR"))
s1<-readJPEG(system.file("images","sim1.jpg",package="dciR"))
r1<-readJPEG(system.file("images","rotated1.jpg",package="dciR"))
d1<-readJPEG(system.file("images","degraded1.jpg",package="dciR"))


c1<-raster(matrix(c1,nrow=dim(c1)[1],ncol=dim(c1)[2]))<0.4
c2<-raster(matrix(c2,nrow=dim(c2)[1],ncol=dim(c2)[2]))<0.4
s1<-raster(matrix(s1,nrow=dim(s1)[1],ncol=dim(s1)[2]))<0.4
r1<-raster(matrix(r1,nrow=dim(r1)[1],ncol=dim(r1)[2]))<0.4
d1<-raster(matrix(d1,nrow=dim(d1)[1],ncol=dim(d1)[2]))<0.4

par(mfrow=c(1,5))
lapply(c(c1,c2,s1,r1,d1),function(x) plot(x,bty="n",box=FALSE,axes=FALSE,legend=FALSE))

#The example below reproduces Figure 5####
ret<-list()
aglist<-4:22

ag_dci<-function(c1,ag){
c1r<-aggregate(c1,ag,fun=median)>0.7
c1mat<-as.matrix(c1r)
time<-system.time(val<-dci(c1mat,1,1))
list(time,val,sum(c1mat))
}
   
for(i in aglist){
ret[[i]]<-ag_dci(c1,i)
}
   
ret<-cbind(matrix(unlist(ret),ncol=7,byrow=T),aglist)

par(mfrow=c(1,2))
plot(ret[,7]/10000,ret[,1],log="y",yaxt="n",xlim=c(0,3),ylim=c(1,10000),xlab="Number of nodes x 10^4",ylab="Computation time (s)")
labels<-sapply(seq(0,3,1),function(i) as.expression(bquote(10^ .(i))))
axis(2,at=c(1,10,100,1000),labels=labels)

plot(ret[,7]/10000,ret[,1],xlab="Number of nodes x 10^4",ylab="")

   
#The example below reproduces Figure 6

ret<-matrix(unlist(lapply(c(c1,c2,s1,d1),function(x) ag_dci(x,16))),ncol=7,byrow=T)
barplot(ret[,6],names.arg=c("Conserved1","Conserved2","Sim1","Degraded1"),ylim=c(0,1),las=2)

References

Larsen, L. G., Choi, J., Nungesser, M. K., & Harvey, J. W. (2012). Directional connectivity in hydrology and ecology. Ecological applications, 22(8), 2204-2220.