# 距离[5]–两样方距离的几种计算方法

vegdist {vegan}

Bray-Curtis similarity index

vegdist(x, method="bray", binary=FALSE, diag=FALSE, upper=FALSE,
na.rm = FALSE, ...)


euclidean

d[jk] = sqrt(sum(x[ij]-x[ik])^2)


binary:

sqrt(A+B-2*J)


manhattan

d[jk] = sum(abs(x[ij] - x[ik]))


binary:

A+B-2*J


gower

d[jk] = (1/M) sum(abs(x[ij]-x[ik])/(max(x[i])-min(x[i])))


binary:

(A+B-2*J)/M,


where M is the number of columns (excluding missing values)

altGower

d[jk] = (1/NZ) sum(abs(x[ij] - x[ik]))


where NZ is the number of non-zero columns excluding double-zeros (Anderson et al. 2006).

binary:

(A+B-2*J)/(A+B-J)


canberra

d[jk] = (1/NZ) sum ((x[ij]-x[ik])/(x[ij]+x[ik]))


where NZ is the number of non-zero entries.

binary:

(A+B-2*J)/(A+B-J)


bray

d[jk] = (sum abs(x[ij]-x[ik]))/(sum (x[ij]+x[ik]))


binary:

(A+B-2*J)/(A+B)


kulczynski

d[jk] 1 - 0.5*((sum min(x[ij],x[ik])/(sum x[ij]) + (sum min(x[ij],x[ik])/(sum x[ik]))


binary:

1-(J/A + J/B)/2


morisita

d[jk] = 1 - 2*sum(x[ij]*x[ik])/((lambda[j]+lambda[k]) * sum(x[ij])*sum(x[ik])), where
lambda[j] = sum(x[ij]*(x[ij]-1))/sum(x[ij])*sum(x[ij]-1)
binary: cannot be calculated


horn Like morisita, but

lambda[j] = sum(x[ij]^2)/(sum(x[ij])^2)


binary: (A+B-2*J)/(A+B)

binomial

d[jk] = sum(x[ij]*log(x[ij]/n[i]) + x[ik]*log(x[ik]/n[i]) - n[i]*log(1/2))/n[i],
where n[i] = x[ij] + x[ik]


binary: log(2)*(A+B-2*J)

cao

d[jk] = (1/S) * sum(log(n[i]/2) - (x[ij]*log(x[ik]) + x[ik]*log(x[ij]))/n[i]),


where S is the number of species in compared sites and n[i] = x[ij] + x[ik]