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)