Nothing

```
#' Permutation Test for cross-type nearest neighbor distances
#' @param dist a distance matrix, the upper n1 x n1 part contains distances between objects of type 1
#' the lower n2 x n2 part contains distances between objects of type 2
#' @param n1 numbers of objects of type 1
#' @param n2 numbers of objects of type 2
#' @param w (optional) weights of the objects (length n1+n2)
#' @param B number of permutations to generate
#' @param alternative alternative hypothesis ("less" to test H0:Colocalization )
#' @param returnSample return sampled null distribution
#' @param parallel Logical. Should we use parallel computing?
#' @param ... additional arguments for mclapply
#' @import stats
#' @return a list with the p.value, the observed weighted mean of the cNN-distances, alternative and (if returnSample) the simulated null dist
#' @author Fabian Scheipl
cnnTest <- function(dist, n1, n2, w = rep(1, n1+n2),
B = 999, alternative = "less", returnSample = TRUE,
parallel = FALSE,
...){
teststat <- function(dist, n1, n2, w){
cnn <- crossNN(dist, n1, n2, w)
return(stats::weighted.mean(x = cnn[,'cnn'], w = cnn[,'w']))
}
obs <- teststat(dist, n1, n2, w)
permutations <- replicate(B, sample(n1+n2), simplify = FALSE)
if(parallel)nulldist <- unlist(parallel::mclapply(permutations, function(x){
teststat(dist[x, x], n1, n2, w[x])
}, ...))
if(!parallel)nulldist <- unlist(lapply(permutations, function(x){
teststat(dist[x, x], n1, n2, w[x])
}, ...))
p.value <- switch(alternative,
greater = mean(obs < nulldist),
less = mean(obs > nulldist),
two.sided = 0.5 * min(mean(obs < nulldist), mean(obs > nulldist)))
ret <- list(statistic = "weighted mean of cNN-distances",
p.value = p.value, estimate = obs, alternative = alternative)
ret$sample <- nulldist
return(ret)
}
```

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