对于较小且可能不相等的组大小，我会采用 chl 和 onestop 的建议并进行蒙特卡洛置换测试。为了使置换测试有效，您需要在以下条件下具有可交换性$H_{0}$. 如果所有分布具有相同的形状（因此在$H_{0}$）， 这是真实的。

这是第一次尝试查看 3 个组且没有关系的情况。首先，让我们比较渐近$\chi^{2}$针对给定组大小的 MC 排列的分布函数（此实现将在更大的组大小时中断）。

P  <- 3                     # number of groups
Nj <- c(4, 8, 6)            # group sizes
N  <- sum(Nj)               # total number of subjects
IV <- factor(rep(1:P, Nj))  # grouping factor
alpha <- 0.05               # alpha-level

# there are N! permutations of ranks within the total sample, but we only want 5000
nPerms <- min(factorial(N), 5000)

# random sample of all N! permutations
# sample(1:factorial(N), nPerms) doesn't work for N! >= .Machine$integer.max
permIdx <- unique(round(runif(nPerms) * (factorial(N)-1)))
nPerms  <- length(permIdx)
H       <- numeric(nPerms)  # vector to later contain the test statistics

# function to calculate test statistic from a given rank permutation
getH <- function(ranks) {
    Rj <- tapply(ranks, IV, sum)
    (12 / (N*(N+1))) * sum((1/Nj) * (Rj-(Nj*(N+1) / 2))^2)
}

# all test statistics for the random sample of rank permutations (breaks for larger N)
# numperm() internally orders all N! permutations and returns the one with a desired index
library(sna)                # for numperm()
for(i in seq(along=permIdx)) { H[i] <- getH(numperm(N, permIdx[i]-1)) }

# cumulative relative frequencies of test statistic from random permutations
pKWH   <- cumsum(table(round(H, 4)) / nPerms)
qPerm  <- quantile(H, probs=1-alpha)  # critical value for level alpha from permutations
qAsymp <- qchisq(1-alpha, P-1)        # critical value for level alpha from chi^2

# illustration of cumRelFreq vs. chi^2 distribution function and resp. critical values
plot(names(pKWH), pKWH, main="Kruskal-Wallis: permutation vs. asymptotic",
     type="n", xlab="h", ylab="P(H <= h)", cex.lab=1.4)
points(names(pKWH), pKWH, pch=16, col="red")
curve(pchisq(x, P-1), lwd=2, n=200, add=TRUE)
abline(h=0.95, col="blue")                         # level alpha
abline(v=c(qPerm, qAsymp), col=c("red", "black"))  # critical values
legend(x="bottomright", legend=c("permutation", "asymptotic"),
       pch=c(16, NA), col=c("red", "black"), lty=c(NA, 1), lwd=c(NA, 2))

现在进行实际的 MC 置换测试。这比较渐近$\chi^{2}$-派生的 p 值，其结果来自coin'soneway_test()和来自上述 MC 排列样本的累积相对频率分布。

> DV1 <- round(rnorm(Nj[1], 100, 15), 2)  # data group 1
> DV2 <- round(rnorm(Nj[2], 110, 15), 2)  # data group 2
> DV3 <- round(rnorm(Nj[3], 120, 15), 2)  # data group 3
> DV  <- c(DV1, DV2, DV3)                 # all data
> kruskal.test(DV ~ IV)                   # asymptotic p-value
Kruskal-Wallis rank sum test
data:  DV by IV 
Kruskal-Wallis chi-squared = 7.6506, df = 2, p-value = 0.02181

> library(coin)                           # for oneway_test()
> oneway_test(DV ~ IV, distribution=approximate(B=9999))
Approximative K-Sample Permutation Test
data:  DV by IV (1, 2, 3) 
maxT = 2.5463, p-value = 0.0191

> Hobs <- getH(rank(DV))                  # observed test statistic

# proportion of test statistics at least as extreme as observed one (+1)
> (pPerm <- (sum(H >= Hobs) + 1) / (length(H) + 1))
[1] 0.0139972

• 您无需检查同方差性。Kruskal 和 Wallis 在他们的原始论文中指出，“测试可能对可变性差异相当不敏感”。
• 如果没有可用的确切测试，您可以使用引导程序。