library(siar)
library(MCMCpack)
library(sna)
library(tnet)
library(igraph)

association <- function (n.birds,test) {
  
  Ass <- matrix(0,nrow=n.birds,ncol=n.birds)
  
  for (i in 1:length(test)){
    
    if (test[i]) {
      Ass[i,test] <- 1
      }
    diag(Ass) <- 0
    colnames(Ass) <- 1:n.birds
    rownames(Ass) <- 1:n.birds
  }
return(Ass)  
}

population<-seq(1:100)
n.birds<-length(population)

samples<-seq(1:20)
n.samples<-length(samples)

Assarr<-array(0,dim=c(n.birds,n.birds,n.samples))

correlations<-matrix(NA,nc=10,nr=20)
correlations2<-matrix(NA,nc=10,nr=20)

for (k in 1:10){

Ass <- matrix(0,nrow=n.birds,ncol=n.birds)

for ( i in 1:n.samples ) {


	#randomise order of individuals in the population for each sampling period
	samplepop<-sample(population,length(population),replace=FALSE)
	
	#create group sizes for that sampling period
	groupsizes<-round(rdirichlet(1,rep(1,20))*100)

	#add to/take from random groups until sum(groupsizes)=population size	
	while(sum(groupsizes)!= n.birds){
		if(sum(groupsizes)>n.birds){
			test<-sample(1:length(groupsizes),1)
                  while(groupsizes[test]==0){
                     test<-sample(1:length(groupsizes),1)
                  }    
			groupsizes[test]<-groupsizes[test]-1
		}
		
		if(sum(groupsizes)<n.birds){
			test<-sample(1:length(groupsizes),1)
			groupsizes[test]<-groupsizes[test]+1
		}

	}

	for ( j in 1:length(groupsizes)){

		#work which individuals are in which group, tests which indivs in the population they match
		#with and then adds association information for those indivs using the function outlined
		#above
		group<-samplepop[(sum(groupsizes[0:(j-1)])+1):sum(groupsizes[0:j])]
		test2<-population %in% group
		Ass<-association(length(population),test2)+Ass
		if(groupsizes[j]>0){
            	tmp.groupdata<-cbind(rep(i,groupsizes[j]),rep(j,groupsizes[j]),group)
			groupdata<-rbind(groupdata,tmp.groupdata)
		}

	}
	

	Assarr[,,i]<-Ass

      correlation<-dcorr(Ass)
      correlations2[i,k]<-correlation

}

}