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# 2022-05-15
# inputs (D,sigma) vector or fitted model or dataframe from predict or list of preceding
# return k = sigma / 100 * sqrt(D) assuming sigma in metres, D in animals/hectare
kfn <- function (object) {
if (is.matrix(object)) {
out <- t(apply(object,1,kfn))
dimnames(out) <- list(rownames(object), c('D','sigma','k'))
out
}
else if (is.numeric(object)) {
D <- object[1]
sigma <- object[2]
c(D = D, sigma = sigma, k = sigma/100 * sqrt(D))
}
else if (is.data.frame(object)) {
if (!all(c('sigma','D') %in% rownames(object))) stop()
sigma <- object['sigma', 'estimate']
D <- object['D','estimate']
c(D = D, sigma = sigma, k = sigma/100 * sqrt(D))
}
else {
if (inherits(object, 'secr')) {
if (object$detectfn %in% c('HN','HHN')) warning("fitted with non-normal detectfn")
object <- predict(object)
kfn(object)
}
else {
if (is.data.frame(object[[1]])) {
t(sapply(object, kfn))
}
else {
# assume list of lists of data.frames
lapply(object, function(x) t(sapply(x, kfn)))
}
}
}
}
# kfn(secrdemo.0)
# [1] 0.6874
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