predict.cobs: Predict method for COBS Fits

Description

Compute predicted values and simultaneous or pointwise confidence bounds for cobs objects.

Usage

# S3 method for cobs
predict(object, z, deriv = 0L,
        minz = knots[1], maxz = knots[nknots], nz = 100,
        interval = c("none", "confidence", "simultaneous", "both"),
        level = 0.95, ...)

Value

a 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.

If z has been specified, it is unchanged in the result.

Arguments

object: object of class cobs.
z: vector of grid points at which the fitted values are evaluated; defaults 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.
deriv: scalar integer specifying (the order of) the derivative that should be computed.
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.

Author

Martin Maechler, based on He and Ng's code in cobs().

Examples

Run this code

example(cobs) # continuing :
(pRbs <- predict(Rbs))
#str(pSbs <- predict(Sbs, xx, interval = "both"))
str(pSbs <- predict(Sbs, xx, interval = "none"))

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)