predict.cobs: Predict method for COBS Fits
In cobs99: Constrained B-splines -- outdated 1999 version

Description Usage Arguments Value Author(s) See Also Examples

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)