我知道HDMD软件包具有一个称为pairwise.mahalanobis的函数，该函数应该计算成对的马氏距离。但是，我也想将权重引入该距离，并且此功能不可行。因此，我开发了自己的代码。为了测试其功能是否良好，我首先使其简单，即没有权重，然后将其结果与pairwise.mahalanobis函数的结果进行比较。但是结果不匹配...下面是我使用的功能：
dist.maha <- function (X) {
diff = pair.diff(X) # pairwise difference of rows
V <- cov(X) ## empirical covariance; positive definite
L <- t(chol(V)) ## lower triangular factor
stdX <- t(forwardsolve(L, t(diff))) # solving the system of linear equations
return(stdX)
}
这是它对玩具数据的两种选择的实现：
data = as.matrix(c(100, 54, 56, 79, 12))
dist_manuel = dist.maha(data)
# This is to convert dist_manuel from a vector to a distance matrix
ind_1 = vector(length = choose(nrow(data),2))
ind_2 = vector(length = choose(nrow(data),2))
k =1
for (j in 1:(nrow(data)-1)){
for(i in (j+1):nrow(data)){
ind_1[k] = i
ind_2[k] = j
k = k + 1
}
}
dist_manuel = cbind(ind_1,ind_2,dist_manuel)
dist_mat = matrix(data = NA, nrow = nrow(data), ncol = nrow(data))
for (j in 1:(nrow(data)-1)){
for(i in (j+1):nrow(data)){
dist_mat[i,j] = dist_manuel[which(dist_manuel[,1] == i & dist_manuel[,2] == j),3]
}
}
# This is the HDMD alternative
id = c(1,2,3,4,5)
data = cbind(id,data)
HDMD = pairwise.mahalanobis(as.data.frame(data[,2]), grouping = data[,1])
dist_HDMD = HDMD$distance
# The outputs
dist_HDMD
# [,1] [,2] [,3] [,4] [,5]
#[1,] 0 1 4 9 16
#[2,] 1 0 1 4 9
#[3,] 4 1 0 1 4
#[4,] 9 4 1 0 1
#[5,] 16 9 4 1 0
dist_mat
# [,1] [,2] [,3] [,4] [,5]
#[1,] NA NA NA NA NA
#[2,] 1.4002541 NA NA NA NA
#[3,] 1.3393735 -0.06088061 NA NA NA
#[4,] 0.6392465 -0.76100768 -0.7001271 NA NA
#[5,] 2.6787470 1.27849290 1.3393735 2.039501 NA
pairwise.mahalanobi函数的结果对我来说似乎是完全荒谬的。对于初学者来说，它为data[2]和data[3]以及data[2]和data[1]分配的距离为1，这在看其值时没有意义。另一方面，我的函数给出一致的结果。例如，让我们比较data[1]和data[2]与data[1]和data[3]之间的距离比率。
（100-54）/（100-56）= 46/44 = 1.045455
现在，这个比例也应该适合我的函数产生的距离。
dist_mat[2,1]/dist_mat[3,1]
#[1] 1.045455
确实如此！这是否表示pairwise.mahalanobis错误时我的功能正常工作？ （或者我是否以某种方式不正确地使用它？）我对R不太熟悉，所以我不敢一个人得出这个结论。如果比我更有经验的人可以确认我的逻辑，那就太好了。
答案 0 :(得分：2)
您的dist.maha函数存在错误。这是显而易见的，因为它计算的某些距离是负数-因此它们不能是实际距离！幸运的是，这很容易解决，因为您只需要平方stdX向量即可。
library("HDMD")
library("tidyverse")
# Convert a vector of pairwise distances from to a distance matrix
# (Simplified approach which doesn't use for-loops)
pairwise_dist_to_dist_matrix <- function(dist, n) {
stopifnot(length(dist) == n*(n-1)/2)
mat <- matrix(NA_real_, n, n)
diag(mat) <- 0
mat[lower.tri(mat)] <- dist
mat
}
dist.maha <- function (X) {
diff <- pair.diff(X) # pairwise difference of rows
V <- cov(X) # empirical covariance; positive definite
L <- t(chol(V)) # lower triangular factor
stdX <- t(forwardsolve(L, t(diff))) # solving the system of linear equations
dist <- stdX * stdX # Don't forget to square!
dist <- rowSums(dist) # And add up the differences in each dimension.
pairwise_dist_to_dist_matrix(dist, nrow(X))
}
# An alternative computation, for an additional check
dist.maha2 <- function(X) {
diff <- pair.diff(X)
V <- cov(X)
Vinv <- solve(V)
dist <- rowSums(diff %*% Vinv * diff)
pairwise_dist_to_dist_matrix(dist, nrow(X))
}
# Slightly more complex data matrix to check if
# functions work in higher dimensions
data <- matrix(c(100, 54, 56, 79, 12, 1, 2, 3, 4, 5), ncol = 2)
dist.maha(data)
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] 0.000000 NA NA NA NA
#> [2,] 2.275210 0.0000000 NA NA NA
#> [3,] 1.974017 0.9742819 0.000000 NA NA
#> [4,] 4.759700 7.5842687 3.250906 0.000000 NA
#> [5,] 7.896067 3.6213875 1.974017 5.690146 0
dist.maha2(data)
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] 0.000000 NA NA NA NA
#> [2,] 2.275210 0.0000000 NA NA NA
#> [3,] 1.974017 0.9742819 0.000000 NA NA
#> [4,] 4.759700 7.5842687 3.250906 0.000000 NA
#> [5,] 7.896067 3.6213875 1.974017 5.690146 0
似乎您还没有正确使用pairwise.mahalanobis。您必须计算并传递协方差矩阵（cov参数）。
# This is the HDMD alternative
id = c(1,2,3,4,5)
# Incorrect:
# You have to specify the `cov` argument.
# Otherwise `pairwise.mahalanobis` doesn't compute it correctly
# as each sample is assumed to be in its own group.
pairwise.mahalanobis(data, grouping = id)$distance
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] 0.000 4345.2805 3840.7349 759.689 15362.940
#> [2,] 4345.280 0.0000 16.7591 1487.209 3369.708
#> [3,] 3840.735 16.7591 0.0000 1194.197 3840.735
#> [4,] 759.689 1487.2090 1194.1967 0.000 9310.174
#> [5,] 15362.940 3369.7076 3840.7349 9310.174 0.000
# Correct:
# NOTE: Ignore the warning message; there seems to be a small bug in `pairwise.mahalanobis`.
pairwise.mahalanobis(data, grouping = id, cov = cov(data))$distance
#> Warning in if (dim(cov) != c(p, p)) stop("cov matrix not of dim = (p,p)
#> \n"): the condition has length > 1 and only the first element will be used
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] 0.000000 2.2752099 1.9740168 4.759700 7.896067
#> [2,] 2.275210 0.0000000 0.9742819 7.584269 3.621388
#> [3,] 1.974017 0.9742819 0.0000000 3.250906 1.974017
#> [4,] 4.759700 7.5842687 3.2509059 0.000000 5.690146
#> [5,] 7.896067 3.6213875 1.9740168 5.690146 0.000000
由reprex package（v0.2.1）于2019-03-24创建