###### the function for correlation of pirwise detaD and distance
COR_detaDd=function(f,d,ncode) { # f is the allellfreq list or allele count
require(diveRsity)
gp=ncode
fr=readGenepop(f, gp, bootstrap = FALSE)
af=fr$allele_freq
DetaD=function(abun,struc){ ## Chao et al, 2017
n=sum(abun);N=ncol(abun);
ga=rowSums(abun);
gp=ga[ga>0]/n;
G=sum(-gp*log(gp))
S=length(gp);
H=nrow(struc);
A=numeric(H-1);W=numeric(H-1);B=numeric(H-1);
Diff=numeric(H-1);Prop=numeric(H-1);
wi=colSums(abun)/n;
W[H-1]=-sum(wi[wi>0]*log(wi[wi>0]));
pi=sapply(1:N,function(k) abun[,k]/sum(abun[,k]))
Ai=sapply(1:N,function(k) -sum(pi[,k][pi[,k]>0]*log(pi[,k][pi[,k]>0])))
A[H-1]=sum(wi*Ai);
if(H>2){
for(i in 2:(H-1)){
I=unique(struc[i,]);NN=length(I);
ai=matrix(0,ncol=NN,nrow=nrow(abun));c
for(j in 1:NN){
II=which(struc[i,]==I[j]);
if(length(II)==1) {ai[,j]=abun[,II];
}else{ai[,j]=rowSums(abun[,II])}
}
pi=sapply(1:NN,function(k) ai[,k]/sum(ai[,k]));
wi=colSums(ai)/sum(ai);
W[i-1]=-sum(wi*log(wi))
Ai=sapply(1:NN,function(k) -sum(pi[,k][pi[,k]>0]*log(pi[,k][pi[,k]>0])))
A[i-1]=sum(wi*Ai);
}
}
Diff[1]=(G-A[1])/W[1];
if(H>2){
for(i in 2:(H-1)){
Diff[i]=(A[i-1]-A[i])/(W[i]-W[i-1]);
}}
Diff=Diff;
out=matrix(c(Diff),ncol=1)
return(out)
}
v1=c("ecosystem","region1","pop1")
v2=c("ecosystem","region1","pop2")
str=data.frame(v1, v2)
str=as.matrix(str)
npops=fr$npops
nloci=fr$nloci
Dmat=list()
for (l in 1:nloci) {
Dmat[[l]]=matrix(data=0,nrow = npops,ncol = npops)
for (i in 1:npops) {
for(j in 1:npops) {
## for every loci has one differentiation value
Dmat[[l]][i,j]=DetaD((af[[l]][,c(i,j)]), str)[2] ### select two pops from allelefrequency
}
}
}
pairwiseDav=Reduce("+", Dmat) / length(Dmat)
#library(popbio)
detaDmat=as.dist(pairwiseDav, diag = FALSE, upper = FALSE)
if (d==TRUE) {
if (is.matrix(d)==TRUE) {
Dgeo=as.dist(d, diag = FALSE, upper = FALSE)
COR_detaDd=cor(detaDmat,d,method = "pearson")
return( COR_detaDd)
}
else {
print("d must be a matrix")
}}
else { d==FALSE
M=matrix(data=0,nrow =npops,ncol = npops)
colnames(M)=fr$pop_names
rownames(M)=fr$pop_names
for (i in 1:npops){
for (j in 1:npops){
M[i,j]=abs(i-j)
}
}
Dgeo=as.dist(M, diag = FALSE, upper = FALSE)
COR_detaDd=cor(detaDmat,Dgeo,method = "pearson")
return(list(PairwiseDetaD=detaDmat,Dgeo=Dgeo,CordetaDd=COR_detaDd))
}
}
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