| mono.con {mgcv} | R Documentation |
Monotonicity constraints for a cubic regression spline
Description
Finds linear constraints sufficient for monotonicity (and
optionally upper and/or lower boundedness) of a cubic regression
spline. The basis representation assumed is that given by the
gam, "cr" basis: that is the spline has a set of knots,
which have fixed x values, but the y values of which constitute the
parameters of the spline.
Usage
mono.con(x,up=TRUE,lower=NA,upper=NA)
Arguments
x |
The array of knot locations. |
up |
If |
lower |
This specifies the lower bound on the spline unless it is
|
upper |
This specifies the upper bound on the spline unless it is
|
Details
Consider the natural cubic spline passing through the points
\{x_i,p_i:i=1 \ldots n \} . Then it is possible
to find a relatively small set of linear constraints on \mathbf{p}
sufficient to ensure monotonicity (and bounds if required):
\mathbf{Ap}\ge\mathbf{b}.
Details are given in Wood (1994).
Value
a list containing constraint matrix A and constraint vector b.
Author(s)
Simon N. Wood simon.wood@r-project.org
References
Gill, P.E., Murray, W. and Wright, M.H. (1981) Practical Optimization. Academic Press, London.
Wood, S.N. (1994) Monotonic smoothing splines fitted by cross validation. SIAM Journal on Scientific Computing 15(5), 1126–1133.
https://www.maths.ed.ac.uk/~swood34/
See Also
Examples
## see ?pcls