| predict.cobs {cobs99} | R Documentation |
Compute predicted values and simultaneous or pointwise confidence
bounds for cobs objects.
## S3 method for class 'cobs':
predict(object,
z, minz = knots[1], maxz = knots[nknots], nz = 100,
interval = c("none", "confidence", "simultaneous", "both"),
level = 0.95, ...)
object |
object of class cobs. |
z |
vector of grid points at which the fitted values are
evaluated; default to an equally spaced grid with nz grid
points between minz and maxz. Note that now z
may lie outside of the knots interval which was not allowed originally. |
minz |
numeric needed if z is not specified; defaults to
min(x) or the first knot if knots are given. |
maxz |
analogous to minz; defaults to max(x) or the
last knot if knots are given. |
nz |
number of grid points in z if that is not given;
defaults to 100. |
interval |
type of interval calculation, see below |
level |
confidence level |
... |
further arguments passed to and from methods. |
predict.cobs produces aa matrix of
predictions and bounds if interval is set (not "none").
The columns are named z, fit, further cb.lo and
cb.up for the simultaneous confidence band, and ci.lo and
ci.up the pointwise confidence intervals according to
specified level.
Martin Maechler, based on He and Ng's code in cobs().
cobs the model fitting function.
example(cobs) # continuing :
(pRbs <- predict(Rbs))
str(pSbs <- predict(Sbs, xx, interval = "both"))
plot(x,y, xlim = range(xx), ylim = range(y, pSbs[,2], finite = TRUE),
main = "COBS Median smoothing spline, automatical lambda")
lines(pSbs, col = "red")
lines(spline(x,f.true), col = "gray40")
matlines(pSbs[,1], pSbs[,-(1:2)],
col= rep(c("green","blue"),c(2,2)), lty=2)