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#' Slope of Rarefunction Curve at Given Sample Size
#'
#' Function evaluates the derivative of the rarefaction
#' function at given sample size. The derivative was
#' directly derived from the expression used in \code{rarefy}.
#'
#' @param x Community counts, either an integer vector for a single
#' site or a data frame or matrix with each row giving site vectors.
#' @param sample Sample sizes where the derivatives are evaluated; can
#' be real
#'
`rareslope` <-
function(x, sample)
{
## 'x' must be integers ('sample' need not be)
if (!identical(all.equal(x, round(x)), TRUE))
stop("community data 'x' must be integers (counts)")
slope <- function(x, sample) {
x <- x[x>0]
J <- sum(x)
## Replace Hurlbert's factorials with gamma() functions and do
## some algebra for derivatives. NB., rarefy() does not use
## factorials but lchoose directly.
d <- digamma(pmax(J-sample+1, 1)) - digamma(pmax(J-x-sample+1, 1))
g <- lgamma(pmax(J-x+1, 1)) + lgamma(pmax(J-sample+1, 1)) -
lgamma(pmax(J-x-sample+1, 1)) - lgamma(J+1)
d <- d*exp(g)
sum(d[is.finite(d)])
}
if (length(dim(x)) == 2)
out <- sapply(sample, function(n) apply(x, 1, slope, sample = n))
else
out <- sapply(sample, function(n) slope(x, sample=n))
out <- drop(out)
if (length(sample) > 1) {
if (is.matrix(out))
colnames(out) <- paste0("N", sample)
else
names(out) <- paste0("N", sample)
}
out
}
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