Nothing
curfitSS <- function(xyw, s=NULL, knots = NULL, n = NULL, from, to,
k = 3, periodic = FALSE, ...) {
eps <- .Machine$double.eps^0.5
# dots <- list(...)
##
## 1. Smoothing spline
##
method <- 'ss'
iopt <- 0
##
## 2. x, y, w
##
x <- xyw$x
y <- xyw$y
w <- xyw$w
m <- length(x)
##
## 3. Smoothing spline (iopt = 0)
##
# if(!is.null(knots) || n > 0)
# warning("supplied 'knots' and/or 'n' ignored for smoothing splines")
# knots <- single(nest)
# n <- 0
{
if(is.null(knots)){
if(is.null(n)){
# If neither knots nor n are supplied, place one knot
# at each distinct x value, plus k extras at the ends
knots <- c(rep(x[1], k), x, rep(x[m], k))
# n <- length(knots)
}
else{
if(n < 2*(k+1))
stop("When knots are supplied, they must include k+1 = ",
k+1, " replicates of the end knots. Only ", n,
" knots were provided, so this condition was violated.",
"\n(When using 'knots.dierckx' for this, specify ",
"interior=FALSE.)")
g2 <- n-2*k
knots <- c(rep(from, k), seq(from, to, length=g2),
rep(to, k) )
}
}
else {
# xb <- min(x)
# from <- min(x) ; per input arguments
# xe <- max(x)
# to <- max(x) ; per input arguments
# if(!is.null(knots)) {
if(!is.numeric(knots))
stop("'knots' must be numeric; is ", class(knots))
knots <- sort(unique(knots))
if(!is.null(n) && (n != length(knots)))
stop("n = ", n, " != length(knots) = ", length(knots),)
# n <- length(knots)
# knots <- knots[(knots > xb + eps) & (knots < xe - eps)]
intknots <- knots[(knots > from + eps) & (knots < to - eps)]
{
if(periodic) {
g <- length(intknots)
if(g >= k)
# Standard periodic boundary knots
# per Dierckx (1993, p. 11)
# knots <- c(knots[rev(g + 1 - (1:k))] - xe + xb,
# xb, knots, xe, knots[1:k] + xe - xb)
knots <- c(intknots[rev(g + 1 - (1:k))] - to + from,
from, intknots, to, intknots[1:k] + to - from)
else
stop("must have at least ", k,
" interior knots for periodic splines")
} # end if(g>=k)
else # end if(periodic)
# Replicate the end knots for coincident boundary knots
# per Dierckx (1993, p. 11)
knots <- c(rep(from, k + 1), intknots, rep(to, k + 1))
}
} # end if(!is.null(knots))
# else {
# Use n
# if(n < 0) stop("'n' must be greater than 0")
# interior <- seq(xb, xe, length = max(0, n - 2 * k))
# interior <- seq(from, to, length = max(0, n - 2 * k))
# knots <- c(rep(xb, k), interior, rep(xe, k))
# knots <- c(rep(from, k), interior, rep(to, k))
# }
}
knots <- as.single(knots)
n <- length(knots)
##
## 4. Finish set-up
##
nest <- as.integer(max(n, 2*k+3, m + k + 2))
coef <- single(nest)
# curfit.f: 'wrk: real array of dimension at least
# (m*(k+1)+nest*(7+3*k)); try double this number.
# curfit.f kills R sometimes with the default lwrk
# AND with double this number.
# lwrk <- as.integer(2 * (m * (k + 1) + nest * (7 + 3 * k)))
#
# the following also killed R: with with(titanium, curfit(x, y))
# lwrk <- as.integer(10 * ((m+n) * (k + 1) + nest * (7 + 3 * k)))
#
lwrk <- as.integer(2 * (m * (k + 1) + nest * (7 + 3 * k)))
wrk <- single(lwrk)
iwrk <- integer(nest)
##
## 5. Fit
##
Knots <- as.single(rep(knots, length=nest) )
val <- {
if(periodic) {
## percur(iopt,m,x,y,w,k,s,nest,n,t,c,fp,wrk,lwrk,iwrk,ier)
.Fortran("percur",
iopt = as.integer(iopt),
m = as.integer(m),
x = as.single(x),
y = as.single(y),
w = as.single(w),
k = k,
s = as.single(s),
nest = as.integer(nest),
n = as.integer(n),
knots = Knots,
coef = coef,
fp = single(1),
wrk = wrk,
lwrk = lwrk,
iwrk = iwrk,
ier = integer(1))
}
else {
## curfit(iopt,m,x,y,w,xb,xe,k,s,nest,n,t,c,fp,wrk,lwrk,iwrk,ier)
# curfit.f documentation: knots 'real array of dimension at least (nest)'
.Fortran("curfit",
iopt = as.integer(iopt),
m = as.integer(m),
x = as.single(x),
y = as.single(y),
w = as.single(w),
from = as.single(from),
to = as.single(to),
k = as.integer(k),
s = as.single(s),
nest = as.integer(nest),
n = as.integer(n),
knots = Knots,
coef = as.single(coef),
fp = single(1),
wrk = wrk,
lwrk = lwrk,
iwrk = iwrk,
ier = integer(1))
}
}
##
## 6. Restore things to double precision so 'update' comparisons will work
## o.w. we might delete the first and / or last observtions
## because 'from' or 'to' picks up round-off in converting to
## to and from single precision ... oops
##
if((length(x)==length(val$x)) &&
all(abs(val$x-x) < 2*eps))
val$x <- x
#
if(is.null(val$from) || (abs(val$from-from) < 2*eps))
val$from <- from
if(is.null(val$to) || (abs(val$to-to) < 2*eps))
val$to <- to
##
## 7. Decode 'ier' error code
##
val$message <- switch(as.character(val$ier),
"0" = character(0),
"-1" = "Spline returned is an interpolating spline.",
"-2" = {
fmt1 <- paste("Spline returned is the weighted",
"least-squares polynomial of degree %d.")
msg1 <- sprintf(fmt1, k)
msg2 <- sprintf("Upper bound on 's' is %f.", val$fp)
paste(msg1, msg2)
},
"1" = {
paste("The required storage exceeds the available",
"storage space specified by 'nest'.")
},
"2" = {
paste("A theoretically impossible result was found; ",
"is 's' too small?")
},
"3" = "The maximum number of iterations (20) has been reached.",
{
msg <- ""
if(val$iopt != 0)
msg <- sprintf("Illegal option %d for a smoothing spline", val$iopt)
if(val$k < 1 || val$k > 5)
msg <- sprintf("%s Illegal 'k' (%d).", msg, val$k)
if(length(val$x) <= val$k)
msg <- sprintf("%s Length 'x' (%d) must be greater than 'k' (%d).",
msg, m, val$k)
if(val$nest < 2 * val$k + 2)
msg <- sprintf("%s Illegal value of 'nest' (%d).",
msg, val$nest)
if(val$lwrk < with(val, (k + 1) * m + nest * (7 + 3 * k)))
msg <- sprintf("%s Illegal value of 'lwrk' (%d).",
msg, val$lwrk)
if(!all(val$w > 0))
msg <- sprintf("%s Some weights nonpositive.", msg)
{
if(s < 0)
msg <- sprintf("%s Negative smoothing 's' (%f) illegal.",
msg, val$s)
else
if(s == 0 && val$nest < m + val$k + 1)
msg <- sprintf("%s Illegal 'nest' (%d).",
msg, val$nest)
}
sub("[ ]+", "", msg)
}
)
if(val$ier > 0) stop(val$message)
##
## 8. Clean up
##
# val$g <- with(val, length(knots[knots > min(x) & knots < max(x)]))
val$g <- with(val, n-2*(k+1))
sing <- names(unlist(sapply(val, attr, "Csingle")))
val[sing] <- lapply(val[sing], as.double)
val$method <- method
val$periodic <- periodic
val$routine <- "curfit.default"
val$xlab <- xyw$xlab
val$ylab <- xyw$ylab
if(length(xyw$xin) > m) {
val$x <- xyw$xin
val$y <- xyw$yin
val$sp <- predict.dierckx(val, newx = xyw$xin)
} else {
val$sp <- fitted(val)
}
class(val) <- "dierckx"
val
}
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