Nothing
generic.vimp.rfcca <- function(object,
...)
{
## check for bootstrap in forest object
if (object$bootstrap == "none") {
stop("when bootstrap is 'none', importance cannot be computed")
}
## object cannot be missing
if (missing(object)) {stop("object is missing!")}
## incoming object must be a grow forest object
if (sum(inherits(object, c("rfcca", "grow"), TRUE) == c(1, 2)) != 2)
stop("this function only works for objects of class '(rfcca, grow)'")
## pull the x, y, z-variables and names from the grow object
xvar <- object$xvar
yvar <- object$yvar
zvar <- object$zvar
zvar.names <- object$zvar.names
## pull the training predictions from the grow object
predicted.oob <- object$predicted.oob
## run rfsrc for predictions
vimpdata <- zvar
vimpdata$t <- predicted.oob
rfvimp <- rfsrc(formula = t~.,
data = vimpdata,
mvdata1 = xvar,
mvdata2 = yvar,
ntree = object$ntree,
mtry = object$mtry,
nodesize = object$nodesize,
nodedepth = object$nodedepth,
nsplit = object$nsplit,
importance = TRUE,
samptype = object$samptype,
sampsize = object$sampsize)
vimp.out <- rfvimp$importance
names(vimp.out) <- zvar.names
## make the output object
rfccaOutput <- list(
call = object$call,
n = object$n,
ntree = object$ntree,
zvar = zvar,
zvar.names = zvar.names,
predicted.oob = predicted.oob,
finalcca = object$finalcca,
importance = vimp.out
)
class(rfccaOutput) <- c("rfcca", "predict")
return(rfccaOutput)
}
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