Nothing
### A pair of functions to handle na.action = na.exclude in cca and
### rda (and capscale in the future?). Function ordiNAexclude finds
### the WA scores for NA constraints if possible, and puts these into
### ordination object. Function ordiNApredict pads the result scores
### with NA or scores if available.
`ordiNAexclude` <-
function(x, excluded)
{
## Check that there is a na.action of class "exclude"
nas <- x$na.action
if (is.null(nas))
return(x)
## add a 'residuals' item, because step, add1.default and
## drop1.default use this to check that number of observations
## does not change in sequential fits.
x$residuals.zombie <- rep(TRUE, max(0, nrow(x$CA$u)))
## rowsums for CA (in RDA/PCA rowsum = NA)
if (!inherits(x, "rda"))
x$rowsum.excluded <- rowSums(excluded)/x$grand.total
## Estimate WA scores for NA cases with newdata of excluded
## observations
if (is.null(x$pCCA) && inherits(nas, "exclude") &&
!inherits(x, "capscale")) {
if (!is.null(x$CCA))
x$CCA$wa.excluded <- predict(x, newdata = excluded,
type = "wa", model = "CCA")
if (!is.null(x$CA))
x$CA$u.excluded <- predict(x, newdata = excluded,
type = "wa", model = "CA")
}
x
}
### Put NA or fitted WA among the scores
`ordiNApredict` <-
function(omit, x)
{
## Only do this if omit is of class "exclude"
if (!inherits(omit, "exclude"))
return(x)
if (!inherits(x, "rda")) {
x$rowsum <- napredict(omit, x$rowsum)
if (inherits(omit, "exclude"))
x$rowsum[omit] <- x$rowsum.excluded
}
if (!is.null(x$CCA)) {
x$CCA$u <- napredict(omit, x$CCA$u)
x$CCA$wa <- napredict(omit, x$CCA$wa)
if (!is.null(x$CCA$wa.excluded))
x$CCA$wa[omit,] <- x$CCA$wa.excluded
}
if (!is.null(x$CA)) {
x$CA$u <- napredict(omit, x$CA$u)
if (!is.null(x$CA$u.excluded))
x$CA$u[omit,] <- x$CA$u.excluded
}
x
}
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