| KernS-methods {lokern} | R Documentation |
Methods for results of glkerns() and
lokerns() which are of (S3) class "KernS".
## S3 method for class 'KernS'
fitted(object, ...)
## S3 method for class 'KernS'
plot(x, type = "l", lwd = 2.5, col = 3, ...)
## S3 method for class 'KernS'
predict(object, x, deriv = object[["deriv"]],
korder = deriv+2, trace.lev = 0, ...)
## S3 method for class 'KernS'
print(x, digits = getOption("digits"), ...)
## S3 method for class 'KernS'
residuals(object, ...)
x,object |
an R object, of S3 class |
type, lwd, col |
arguments for |
deriv |
integer, >= 0, specifiying order of derivative that should be predicted. |
korder |
nonnegative integer giving the kernel order; see
|
digits |
number of significant digits, see |
trace.lev |
integer; level of tracing of Fortran level
computations; see |
... |
potentially further arguments passed to and from
methods. For the |
Note that fitted() and residuals() rely on
x.inOut having been true or x.out having contained the
data x, in the lokerns or glkerns
call.
The plot() method calls plotDS from
package sfsmisc.
predict(object, x, deriv) when either some x are not in
x.out or deriv is not 0, basically recalls the original
lokerns or glkerns function (keeping the
bandwidths for lokerns).
(differing, depending on the generic function)
## "interesting" artificial data:
set.seed(47)
x <- sort(round(10*runif(250),2))
fx <- 5 - x/2 + 3*exp(-(x-5)^2)
y <- fx + rnorm(fx)/4
plot(x,y)
lof <- lokerns(x,y, trace=1)# tracing the phases inside the Fortran code
plot(lof)
plot(lof, cex = 1/4)# maybe preferable
## Simpler, using the lines() method:
plot(x,y); lines(lof, lwd=2, col=2)
qqnorm(residuals(lof)) # hmm... overfitting?
stopifnot(all.equal(y, fitted(lof) + residuals(lof), tolerance = 1e-15),
predict(lof)$y == fitted(lof))
lof$iter # negative ?
tt <- seq(0, 10, by=1/32)
p0 <- predict(lof, x=tt)
p1 <- predict(lof, x=tt, deriv=1)
p2 <- predict(lof, x=tt, deriv=2)
plot(p2, type="l"); abline(h=0, lty=3) # not satisfactory:
lof2 <- lokerns(x,y, deriv=2)
plot(lof2, main=
"lokerns(*, deriv=2) -- much more smooth than predict(*,deriv=2)")
lines(p2); abline(h=0, lty=3)