Nothing
`envfit.coca` <- function(ord, env,
which = c("response", "predictor"),
choices = c(1,2),
scaling = FALSE,
w,
na.rm = FALSE,
strata = NULL,
permutations = 999, ...) {
## get weights
if(missing(w)) {
w <- weights(ord)
}
vectors <- factors <- seed <- NULL
## what are we plotting, response or predictor?
which <- match.arg(which)
## and map to X and Y for extraction
WHICH <- ifelse(which == "response", "Y", "X")
## should the scores be rescaled - only for species though
if(is.logical(scaling))
scaling <- ifelse(scaling, 2, 1)
X <- scores(ord, display = "sites", choices = choices,
scaling = scaling)
## then extract the response or predictor scores
X <- lapply(X, `[[`, WHICH)[[1]]
keep <- complete.cases(X) & complete.cases(env)
if (any(!keep)) {
if (!na.rm)
stop("missing values in data: consider na.rm = TRUE")
X <- X[keep, , drop=FALSE]
env <- env[keep, , drop=FALSE]
na.action <- structure(seq_along(keep)[!keep], class="omit")
}
if (is.data.frame(env)) {
facts <- unlist(lapply(env, is.factor))
if (sum(facts)) {
Pfac <- env[, facts, drop = FALSE]
P <- env[, !facts, drop = FALSE]
if (length(P)) {
if (permutations) {
if (!exists(".Random.seed", envir = .GlobalEnv,
inherits = FALSE)) {
runif(1)
}
seed <- get(".Random.seed", envir = .GlobalEnv,
inherits = FALSE)
}
vectors <- vectorfit(X, P, permutations, strata,
choices, w = w, ...)
}
if (!is.null(seed)) {
assign(".Random.seed", seed, envir = .GlobalEnv)
}
factors <- factorfit(X, Pfac, permutations, strata,
choices, w = w, ...)
sol <- list(vector = vectors, factors = factors)
}
else vectors <- vectorfit(X, env, permutations, strata,
choices, w = w, ...)
} else {
vectors <- vectorfit(X, env, permutations, strata,
choices, w = w, ...)
}
sol <- list(vectors = vectors, factors = factors)
if (!is.null(na.action))
sol$na.action <- na.action
class(sol) <- "envfit"
sol
}
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