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Smoothing spline

**Note**

For a simpler but less flexible method to generate smoothing splines,
try the Curve Fitting app or the `fit`

function.

returns the B-form of the smoothest function `sp`

= spaps(`x`

,`y`

,`tol`

) *f* that lies
within the given tolerance `tol`

of the given data points
`(x(j), y(:,j)), j=1:length(x)`

. The data values
`y(:,j)`

are scalars, vectors, matrices, or even ND-arrays.
Data points with the same data site are replaced by their weighted average, with
its weight the sum of the corresponding weights, and the tolerance
`tol`

is reduced accordingly.

`[sp,`

also returns the smoothed values. `values`

] = spaps(x,y,tol) `values`

is the same as
`fnval(sp,x)`

.

Here, the distance of the function *f* from the given data is
measured by

$$E(f)={\displaystyle \sum _{j=1}^{n}w(j)|(y(:,j)-f(x(j))){|}^{2}}$$

with the default choice for the weights `w`

making
*E*(*f*) the composite trapezoidal rule
approximation to $$\int}_{\mathrm{min}(x)}^{\mathrm{max}(x)}|y-f{|}^{2$$, and |*z*|^{2}
denoting the sum of squares of the entries of *z*.

Further, *smoothest* means that the following roughness
measure is minimized:

$$F({D}^{m}f)={\displaystyle \underset{\mathrm{min}(x)}{\overset{\mathrm{max}(x)}{\int}}\lambda (t)|{D}^{m}}f(t)|{}^{2}dt$$

where *D ^{m}f* denotes the

`m`

th derivative of `m`

is `2`

, the default value for the
roughness measure weight When `tol`

is nonnegative, then the spline
*f* is determined as the unique minimizer of the expression
ρ*E*(*f*) +
*F*(*D ^{m}f*),
with the smoothing parameter ρ (optionally returned) so chosen that

`tol`

.
Hence, when `m`

is `2`

, then, after conversion
to ppform, the result should be the same (up to round-off) as obtained by
csaps(`tol`

is zero, then the “natural” or
variational spline interpolant of order 2`tol`

, the least-squares approximation to the data
by polynomials of degree <`m`

is returned.When `tol`

is negative, then ρ is
`-tol`

.

The default value for the weight function *λ* in the
roughness measure is the constant function 1. But you can choose it to be, more
generally, a piecewise constant function, with breaks only at the data sites.
Assuming the vector `x`

to be strictly increasing, you specify
such a piecewise constant *λ* by inputting
`tol`

as a vector of the same size as `x`

.
In that case, `tol(i)`

is taken as the constant value of
*λ* on the interval (`x(i-1)`

..
`x(i)`

), `i=2:length(x)`

, while
`tol(1)`

continues to be used as the specified
tolerance.

`[sp,values,`

also returns the actual value of ρ used as the third output argument.`rho`

] = spaps(x,y,tol)

`[...] = spaps(x,y,tol,`

lets you specify the weight vector `w`

,`m`

) `w`

and/or the integer
`m`

, by supplying them as an `argi`

. For
this, `w`

must be a nonnegative vector of the same size as
`x`

; `m`

must be `1`

(for
a piecewise linear smoothing spline), or `2`

(for the default
cubic smoothing spline), or `3`

(for a quintic smoothing
spline).

If the resulting smoothing spline, sp, is to be evaluated outside its basic
interval, it should be replaced by `fnxtr(sp,m)`

to ensure that
its `m`

-th derivative is zero outside that interval.

`[...] = spaps({x1,...,xr},y,tol,...) `

returns the B-form of an `r`

-variate tensor-product smoothing
spline that is roughly within the specified tolerance to the given
*gridded data*. For *scattered* data,
use `tpaps`

. Now `y`

is expected to supply the
corresponding gridded values, with `size(y)`

equal to
`[length(x1),...,length(xr)] `

in case the function is
scalar-valued, and equal to `[d,length(x1),...,length(xr)]`

in
case the function is `d`

-valued. Further,
`tol`

must be a cell array with `r`

entries, with `tol{i}`

the tolerance used during the
`i`

-th step when a univariate (but vector-valued) smoothing
spline in the `i`

-th variable is being constructed. The
optional input for `m `

must be an `r`

-vector
(with entries from the set `{1,2,3}`

), and the optional input
for `w `

must be a cell array of length `r`

,
with `w{i}`

either empty (to indicate that the default choice
is wanted) or else a positive vector of the same length as
`xi`

.

This function uses the Reinsch's approach [1] , including his way of choosing the equation for the optimal smoothing parameter in such a way that a good initial guess is available and Newton's method is guaranteed to converge and to converge fast.

[1] C. Reinsch. "Smoothing by
spline functions." *Numer. Math*. 10 (1967), 177–183.