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`renyiaccum` <-
function(x, scales=c(0, 0.5, 1, 2, 4, Inf), permutations = 100,
raw = FALSE, collector = FALSE, subset, ...)
{
if (!missing(subset))
x <- subset(x, subset)
x <- as.matrix(x)
n <- nrow(x)
p <- ncol(x)
if (p==1) {
x <- t(x)
n <- nrow(x)
p <- ncol(x)
}
pmat <- getPermuteMatrix(permutations, n)
permutations <- nrow(pmat)
m <- length(scales)
result <- array(dim=c(n,m,permutations))
dimnames(result) <- list(pooled.sites=c(1:n), scale=scales,
permutation=c(1:permutations))
for (k in 1:permutations) {
result[,,k] <- as.matrix(renyi((apply(x[pmat[k,],],2,cumsum)),
scales=scales, ...))
}
if (raw)
collector <- FALSE
if (collector)
ref <- as.matrix(renyi(apply(x, 2, cumsum), scales = scales, ...))
if (raw) {
if (m==1) {
result <- result[,1,]
}
}else{
tmp <- array(dim=c(n,m,6 + as.numeric(collector)))
for (i in 1:n) {
for (j in 1:m) {
tmp[i,j,1] <- mean(result[i,j,1:permutations])
tmp[i,j,2] <- sd(result[i,j,1:permutations])
tmp[i,j,3] <- min(result[i,j,1:permutations])
tmp[i,j,4] <- max(result[i,j,1:permutations])
tmp[i,j,5] <- quantile(result[i,j,1:permutations],0.025)
tmp[i,j,6] <- quantile(result[i,j,1:permutations],0.975)
if (collector)
tmp[i,j,7] <- ref[i,j]
}
}
result <- tmp
dimnames(result) <- list(pooled.sites=c(1:n),
scale=scales,
c("mean", "stdev", "min", "max", "Qnt 0.025", "Qnt 0.975", if (collector) "Collector"))
}
attr(result, "control") <- attr(pmat, "control")
class(result) <- c("renyiaccum", class(result))
result
}
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