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Least-squares spline approximation
spap2(knots,k,x,y)
spap2(l,k,x,y)
sp = spap2(...,x,y,w)
spap2({knorl1,...,knorlm},k,{x1,...,xm},y)
spap2({knorl1,...,knorlm},k,{x1,...,xm},y,w)
spap2(knots,k,x,y) returns the B-form of the spline f of order k with the given knot sequence knots for which
(*) y(:,j) = f(x(j)), all j
in the weighted mean-square sense, meaning that the sum
$$\sum _{j}w(j)|y(:,j)-f\left(x(j)\right){|}^{2}$$
is minimized, with default weights equal to 1. The data values y(:,j) may be scalars, vectors, matrices, even ND-arrays, and |z|^{2} stands for the sum of the squares of all the entries of z. Data points with the same site are replaced by their average.
If the sites x satisfy the (Schoenberg-Whitney) conditions
$$\begin{array}{l}\text{knots}(j)x(j)\text{knots}(j+k)\\ (**)\text{}j=1,\mathrm{...},\text{length}(x)=\text{length(knots)}-k\end{array}$$
then there is a unique spline (of the given order and knot sequence) satisfying (*) exactly. No spline is returned unless (**) is satisfied for some subsequence of x.
spap2(l,k,x,y) , with l a positive integer, returns the B-form of a least-squares spline approximant, but with the knot sequence chosen for you. The knot sequence is obtained by applying aptknt to an appropriate subsequence of x. The resulting piecewise-polynomial consists of l polynomial pieces and has k-2 continuous derivatives. If you feel that a different distribution of the interior knots might do a better job, follow this up with
sp1 = spap2(newknt(sp),k,x,y));
sp = spap2(...,x,y,w) lets you specify the weights w in the error measure (given above). w must be a vector of the same size as x, with nonnegative entries. All the weights corresponding to data points with the same site are summed when those data points are replaced by their average.
spap2({knorl1,...,knorlm},k,{x1,...,xm},y) provides a least-squares spline approximation to gridded data. Here, each knorli is either a knot sequence or a positive integer. Further, k must be an m-vector, and y must be an (r+m)-dimensional array, with y(:,i1,...,im) the datum to be fitted at the site [x{1}(i1),...,x{m}(im)], all i1, ..., im. However, if the spline is to be scalar-valued, then, in contrast to the univariate case, y is permitted to be an m-dimensional array, in which case y(i1,...,im) is the datum to be fitted at the site [x{1}(i1),...,x{m}(im)], all i1, ..., im.
spap2({knorl1,...,knorlm},k,{x1,...,xm},y,w) also lets you specify the weights. In this m-variate case, w must be a cell array with m entries, with w{i} a nonnegative vector of the same size as xi, or else w{i} must be empty, in which case the default weights are used in the ith variable.
sp = spap2(augknt([a,xi,b],4),4,x,y)
is the least-squares approximant to the data x, y, by cubic splines with two continuous derivatives, basic interval [a..b], and interior breaks xi, provided xi has all its entries in (a..b) and the conditions (**) are satisfied in some fashion. In that case, the approximant consists of length(xi)+1 polynomial pieces. If you do not want to worry about the conditions (**) but merely want to get a cubic spline approximant consisting of l polynomial pieces, use instead
sp = spap2(l,4,x,y);
If the resulting approximation is not satisfactory, try using a larger l. Else use
sp = spap2(newknt(sp),4,x,y);
for a possibly better distribution of the knot sequence. In fact, if that helps, repeating it may help even more.
As another example, spap2(1, 2, x, y); provides the least-squares straight-line fit to data x,y, while
w = ones(size(x)); w([1 end]) = 100; spap2(1,2, x,y,w);
forces that fit to come very close to the first data point and to the last.