# predict.cobs: Predict method for COBS Fits In cobs: Constrained B-Splines (Sparse Matrix Based)

## Description

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

## Usage

 ```1 2 3 4 5``` ```## S3 method for class 'cobs' predict(object, z, deriv = 0L, minz = knots, maxz = knots[nknots], nz = 100, interval = c("none", "confidence", "simultaneous", "both"), level = 0.95, ...) ```

## 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.

## 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.

## Author(s)

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

`cobs` the model fitting function.
 ``` 1 2 3 4 5 6 7 8 9 10 11``` ```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) ```