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## smoothSpline: smootherFcn supplied to prcurve
## again a wrapper but allow us to specify De'ath's recommended
## smoother strategy
`smoothSpline` <- function(lambda, x, choose = TRUE,
complexity, ..., penalty = 1,
cv = FALSE, keep.data = FALSE,
control.spar = list(low = 0)) {
## complexity is the 'df' argument
## choose selects whether to use fixed complexity or allow
## underlying fitting function to return complexity
ord <- order(lambda)
lambda <- lambda[ord]
x <- x[ord]
if(choose) { ## choose complexity
f <- smooth.spline(lambda, x, ...,
penalty = penalty,
keep.data = keep.data, cv = cv,
control.spar = control.spar)
} else { ## use specified complexity
f <- smooth.spline(lambda, x, ..., df = complexity,
penalty = penalty, ## no cv as specifying df
keep.data = keep.data,
control.spar = control.spar)
}
p <- predict(f, x=lambda)$y
res <- list(lambda = lambda, x = x, fitted.values = p,
complexity = f$df, model = f)
class(res) <- "prcurveSmoother"
res
}
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