# Determining the sample size based on the amount of coverage; systematic more coverage
Sample.Cover.Sys.more.ftn <- function(Pop.mat, Cover.percent, index){
	# Arranging the data
	Pop.Cov	    	<- data.frame(Pop.mat[index,],row.names=NULL)
	names(Pop.Cov) 	<- NULL
	Pop.Cov 	<- as.matrix(Pop.Cov)
	Pop.Sum     	<- apply(Pop.Cov,2,sum)
	Pop.Cov     	<- Pop.Cov[,Pop.Sum>0,drop=F]
	Pop.mean    	<- apply(Pop.Cov,2,mean)

	# Finding the starting vector (see write up for elaboration on how this works)
	Cover            <- length(Pop.Cov[1,Pop.Cov[1,]>0])/length(Pop.mean)
	Pop.Cover.list   <- 1
	for(i in 2:length(Pop.Cov[,1])){
	   if(length(Pop.Cov[i,Pop.Cov[i,]>0])/length(Pop.mean) > Cover){
	      Cover <- length(Pop.Cov[i,Pop.Cov[i,]>0])/length(Pop.mean)
              Pop.Cover.list <- i
	   }
	}
	Pop.Cover       		<- Pop.Cov[Pop.Cover.list,]
	Pop.Cov.mat     		<- Pop.Cov
	Pop.Cov.mat[Pop.Cover.list,]    <- 0

	while(Cover < Cover.percent){
	   j <- 1
	   for(i in 1:length(Pop.Cov.mat[,1])){
	      Sum.Cover   <- Pop.Cov.mat[i,]+Pop.Cover
	      if(length(Sum.Cover[Sum.Cover>0])/length(Pop.mean) > Cover){
	         Cover <- length(Sum.Cover[Sum.Cover>0])/length(Pop.mean)
                 j     <- i
	      }
	   }
	   Pop.Cover	    <- Pop.Cover+Pop.Cov.mat[j,]
	   Pop.Cover.list   <- c(Pop.Cover.list, j)
   	   Pop.Cov.mat[j,]  <- 0
	}
	return(list(index[Pop.Cover.list],Cover))
}