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##' @title Compare proxies across two data sets
##'
##' @description.. content for \description{} (no empty lines) ..
##'
##' @details To Do
##'
##' @param x data frame; training set samples to compare against
##' @param ... other arguments passed to methods.
##'
##' @return To Do
##'
##' @author Gavin L. Simpson
##'
##' @rdname compare
##'
`compare` <- function(x, ...) {
UseMethod("compare")
}
##' @param y data frame; passive or core samples
##' @param env numeric vector of environmental or contraint data for residual length ordination
##' @param by character; compare data sets by sites or species (proxies).
##' @param ordination character; which constrained ordination method to use
##' @param method character; which dissimilarity method to use. See \code{distance}.
##' @param transform character; should a transformation be applied to the data. Ignored.
##' @param n2limit integer; the criterion for indicating species with potentially
##' poorly estimated optima. The default value of \code{5L} is one suggested by
##' R. Telford. If not provided as an integer, \code{n2limit} will be coerced to one.
##'
##' @rdname compare
##'
`compare.default` <- function(x, y, env,
by = c("sites", "species"),
ordination = "rda",
method = "chord",
transform = NULL,
n2limit = 5L,
...) {
joint <- join(x, y, split = TRUE)
x2 <- joint$x
y2 <- joint$y
if (!is.integer(n2limit)) {
n2limit <- as.integer(n2limit)
}
n2.x <- n2(x2, which = "species")
by <- match.arg(by)
if (identical(by, "species")) {
n2.y <- n2(y2, which = "species")
cny <- colnames(y)
cnx <- colnames(x)
out <- data.frame(inTrain = cny %in% cnx,
n2 = n2.y[cny],
n2Train = n2.x[cny],
max = apply(y2, 2, max)[cny],
maxTrain = apply(x2, 2, max)[cny])
} else {
out <-
data.frame(sumMissing = rowSums(y2[, n2.x == 0L]),
sumPoorOpt = rowSums(y2[, n2.x <= n2limit]),
closestSamp = minDC(ana.fit <- analog(x2, y2))$minDC)
if (!missing(env)) {
out <- cbind(out,
residLen = residLen(x, env = env, passive = y,
method = ordination)[["passive"]])
}
}
out
}
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