Nothing
dis.nness.find.m <- function(comm, ness=FALSE){
min1 <- min(rowSums(comm))
if(ness==TRUE){
if(min1 < 61){
ms <- 1:(min1/2)
} else{
ms <- round(seq(1, (min1/2), length.out=30), digits=0)
}
}
if(ness==FALSE){
if(min1 < 31){
ms <- 1:min1
} else{
ms <- round(seq(1, min1, length.out=30), digits=0)
}
}
comp <- (nrow(comm)*(nrow(comm)-1))/2
ms.n <- length(ms)
dists.m <- matrix(, comp, ms.n)
for(i in 1:ms.n){
dists.m[, i] <- as.vector(dis.nness(comm, m=ms[i], ness=ness))
}
#print(dists.m)
kendall.resu <- cor(dists.m, method="kendall")
rownames(kendall.resu) <- ms
colnames(kendall.resu) <- ms
#print(kendall.resu)
dif1 <- kendall.resu[, 1] - kendall.resu[, ms.n] #correlations to m=1 and to the highest m.
dif2 <- abs(dif1)
posi <- which.min(dif2) # the position of the m-value which makes cor to m=1 and to m=max most similar (minimal difference).
m <- ms[posi]
return(m)
}
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