Nothing
##" Individual based accumulation model. Similar to poolaccum but uses
##estimateR. Inherits from "poolaccum" class and uses its methods.
`estaccumR` <-
function(x, permutations = 100, parallel = getOption("mc.cores"))
{
n <- nrow(x)
N <- seq_len(n)
estFun <- function(idx) {
estimateR(apply(x[idx,], 2, cumsum))[c(1,2,4),]
}
permat <- getPermuteMatrix(permutations, n)
nperm <- nrow(permat)
## parallel processing
if (is.null(parallel))
parallel <- 1
hasClus <- inherits(parallel, "cluster")
if (hasClus || parallel > 1) {
if(.Platform$OS.type == "unix" && !hasClus) {
tmp <- mclapply(1:nperm, function(i)
estFun(permat[i,]),
mc.cores = parallel)
} else {
if (!hasClus) {
parallel <- makeCluster(parallel)
}
tmp <- parLapply(parallel, 1:nperm, function(i) estFun(permat[i,]))
if (!hasClus)
stopCluster(parallel)
}
} else {
tmp <- lapply(1:nperm, function(i) estFun(permat[i,]))
}
S <- sapply(tmp, function(x) x[1,])
chao <- sapply(tmp, function(x) x[2,])
ace <- sapply(tmp, function(x) x[3,])
means <- cbind(N = N, S = rowMeans(S), Chao = rowMeans(chao),
ACE = rowMeans(ace))
out <- list(S = S, chao = chao, ace = ace, N = N, means = means)
attr(out, "control") <- attr(permat, "control")
class(out) <- c("estaccumR", "poolaccum")
out
}
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