NAG Library Function Document

nag_1d_spline_fit (e02bec)

nag_1d_spline_fit (e02bec) computes a cubic spline approximation to an arbitrary set of data points. The knots of the spline are located automatically, but a single argument must be specified to control the trade-off between closeness of fit and smoothness of fit.

2 Specification

#include <nag.h>

#include <nage02.h>

void	nag_1d_spline_fit (Nag_Start start, Integer m, const double x[], const double y[], const double weights[], double s, Integer nest, double fp, Nag_Comm warmstartinf, Nag_Spline spline, NagError fail)

3 Description

nag_1d_spline_fit (e02bec) determines a smooth cubic spline approximation

s (x)

to the set of data points

(x_{r}, y_{r})

, with weights

w_{r}

, for

r = 1, 2, \dots, m

The spline is given in the B-spline representation

s (x) = \sum_{i = 1}^{n - 4} c_{i} N_{i} (x),

(1)

where

N_{i} (x)

denotes the normalized cubic B-spline defined upon the knots

λ_{i}, λ_{i + 1}, \dots, λ_{i + 4}

The total number

n

of these knots and their values

λ_{1}, \dots, λ_{n}

are chosen automatically by the function. The knots

λ_{5}, \dots, λ_{n - 4}

are the interior knots; they divide the approximation interval

[x_{1}, x_{m}]

into

n - 7

sub-intervals. The coefficients

c_{1}, c_{2}, \dots, c_{n - 4}

are then determined as the solution of the following constrained minimization problem:

minimize

η = \sum_{i = 5}^{n - 4} δ_{i}^{2}

(2)

subject to the constraint

θ = \sum_{r = 1}^{m} ε_{r}^{2} \leq S,

(3)

where	$δ_{i}$	stands for the discontinuity jump in the third order derivative of $s (x)$ at the interior knot $λ_{i}$ ,
	$ε_{r}$	denotes the weighted residual $w_{r} (y_{r} - s (x_{r}))$ ,
and	$S$	is a non-negative number to be specified by you.

The quantity

η

can be seen as a measure of the (lack of) smoothness of

s (x)

, while closeness of fit is measured through

θ

. By means of the argument

S

, ‘the smoothing factor’, you can then control the balance between these two (usually conflicting) properties. If

S

is too large, the spline will be too smooth and signal will be lost (underfit); if

S

is too small, the spline will pick up too much noise (overfit). In the extreme cases the function will return an interpolating spline

(θ = 0)

S

is set to zero, and the weighted least squares cubic polynomial

(η = 0)

S

is set very large. Experimenting with

S

values between these two extremes should result in a good compromise. (See Section 9.2 for advice on choice of

S

The method employed is outlined in Section 9.3 and fully described in Dierckx (1975), Dierckx (1981) and Dierckx (1982). It involves an adaptive strategy for locating the knots of the cubic spline (depending on the function underlying the data and on the value of

S

), and an iterative method for solving the constrained minimization problem once the knots have been determined.

Values of the computed spline, or of its derivatives or definite integral, can subsequently be computed by calling nag_1d_spline_evaluate (e02bbc), nag_1d_spline_deriv (e02bcc) or nag_1d_spline_intg (e02bdc), as described in Section 9.4.

4 References

Dierckx P (1975) An algorithm for smoothing, differentiating and integration of experimental data using spline functions J. Comput. Appl. Math. 1 165–184

Dierckx P (1981) An improved algorithm for curve fitting with spline functions Report TW54 Department of Computer Science, Katholieke Univerciteit Leuven

Dierckx P (1982) A fast algorithm for smoothing data on a rectangular grid while using spline functions SIAM J. Numer. Anal. 19 1286–1304

Reinsch C H (1967) Smoothing by spline functions Numer. Math. 10 177–183

5 Arguments

1: start – Nag_StartInput

On entry: must be set to

Nag_Cold

Nag_Warm

$start = Nag_Cold$: The function will build up the knot set starting with no interior knots. No values need be assigned to the argument $spline \to n$ , and memory will be allocated internally to $spline \to lamda$ , $spline \to c$ , $warmstartinf \to nag_w$ and $warmstartinf \to nag_iw$ .
$start = Nag_Warm$: The function will restart the knot-placing strategy using the knots found in a previous call of the function. In this case, all arguments except s must be unchanged from that previous call. This warm start can save much time in searching for a satisfactory value of the smoothing factor $S$ .

Constraint:

start = Nag_Cold

Nag_Warm

2: m – IntegerInput

On entry:

m

, the number of data points.

Constraint:

m \geq 4

3: x[m] – const doubleInput

On entry:

x [r - 1]

holds the value

x_{r}

of the independent variable (abscissa)

x

, for

r = 1, 2, \dots, m

Constraint:

x_{1} < x_{2} < \dots < x_{m}

4: y[m] – const doubleInput

On entry:

y [r - 1]

holds the value

y_{r}

of the dependent variable (ordinate)

y

, for

r = 1, 2, \dots, m

5: weights[m] – const doubleInput

On entry: the values

w_{r}

of the weights, for

r = 1, 2, \dots, m

. For advice on the choice of weights, see Section 2.1.2 in the e02 Chapter Introduction.

Constraint:

weights [r - 1] > 0.0

, for

r = 1, 2, \dots, m

6: s – doubleInput

On entry: the smoothing factor,

S

S = 0.0

, the function returns an interpolating spline.

S

is smaller than machine precision, it is assumed equal to zero.

For advice on the choice of

S

, see Sections 3 and 9.2.

Constraint:

s \geq 0.0

7: nest – IntegerInput

On entry: an overestimate for the number,

n

, of knots required.

Constraint:

nest \geq 8

. In most practical situations,

nest = m / 2

is sufficient. nest never needs to be larger than

m + 4

, the number of knots needed for interpolation

(s = 0.0)

8: fp – double *Output

On exit: the sum of the squared weighted residuals,

θ

, of the computed spline approximation. If

fp = 0.0

, this is an interpolating spline. fp should equal

s

within a relative tolerance of

0.001

unless

n = 8

when the spline has no interior knots and so is simply a cubic polynomial. For knots to be inserted,

s

must be set to a value below the value of fp produced in this case.

9: warmstartinf – Nag_Comm *

Pointer to structure of type Nag_Comm with the following members:

nag_w – double *Input: On entry: if the warm start option is used, the values $nag_w [0]$ , $\dots$ , $nag_w [spline \to n - 1]$ must be left unchanged from the previous call.

nag_iw – Integer *Input: On entry: if the warm start option is used, the values $nag_iw [0]$ , $\dots$ , $nag_iw [spline \to n - 1]$ must be left unchanged from the previous call.

Note that when the information contained in the pointers

nag_w

and

nag_iw

is no longer of use, or before a new call to nag_1d_spline_fit (e02bec) with the same warmstartinf, you should free this storage using the NAG macro NAG_FREE. This storage will have been allocated only if this function returns with

fail . code = NE_NOERROR

, NE_SPLINE_COEFF_CONV or NE_NUM_KNOTS_1D_GT.

10: spline – Nag_Spline *

Pointer to structure of type Nag_Spline with the following members:

n – IntegerInput/Output: On entry: if the warm start option is used, the value of $n$ must be left unchanged from the previous call.

On exit: the total number, $n$ , of knots of the computed spline.

lamda – double *Input/Output: On entry: a pointer to which, if $start = Nag_Cold$ , memory of size nest is internally allocated. If the warm start option is used, the values $lamda [0]$ , $lamda [1]$ , $\dots$ , $lamda [n - 1]$ must be left unchanged from the previous call.

On exit: the knots of the spline, i.e., the positions of the interior knots $lamda [4]$ , $lamda [5]$ , $\dots$ , $lamda [n - 5]$ as well as the positions of the additional knots $lamda [0] = lamda [1] = lamda [2] = lamda [3] = x [0]$ and $lamda [n - 4] = lamda [n - 3] = lamda [n - 2] = lamda [n - 1] = x [m - 1]$ needed for the B-spline representation.

c – double *Output: On exit: a pointer to which, if $start = Nag_Cold$ , memory of size $nest - 4$ is internally allocated. $c [i - 1]$ holds the coefficient $c_{i}$ of the B-spline $N_{i} (x)$ in the spline approximation $s (x)$ , for $i = 1, 2, \dots, n - 4$ .

Note that when the information contained in the pointers

lamda

and

c

is no longer of use, or before a new call to nag_1d_spline_fit (e02bec) with the same spline, you should free this storage using the NAG macro NAG_FREE. This storage will have been allocated only if this function returns with

fail . code = NE_NOERROR

, NE_SPLINE_COEFF_CONV, or NE_NUM_KNOTS_1D_GT.

11: fail – NagError *Input/Output

The NAG error argument (see Section 3.6 in the Essential Introduction).

6 Error Indicators and Warnings

NE_ALLOC_FAIL: Dynamic memory allocation failed.
NE_BAD_PARAM: On entry, argument start had an illegal value.
NE_ENUMTYPE_WARM: $start = Nag_Warm$ at the first call of this function. start must be set to $start = Nag_Cold$ at the first call.
NE_INT_ARG_LT: On entry, $m = ⟨value⟩$ .
Constraint: $m \geq 4$ .
On entry, $nest = ⟨value⟩$ .
Constraint: $nest \geq 8$ .
NE_NOT_STRICTLY_INCREASING: The sequence x is not strictly increasing: $x [⟨value⟩] = ⟨value⟩$ , $x [⟨value⟩] = ⟨value⟩$ .
NE_NUM_KNOTS_1D_GT: The number of knots needed is greater than nest, $nest = ⟨value⟩$ . If nest is already large, say $nest > m / 2$ , this may indicate that possibly s is too small: $s = ⟨value⟩$ .
NE_REAL_ARG_LT: On entry, $s = ⟨value⟩$ .
Constraint: $s \geq 0.0$ .
NE_SF_D_K_CONS: On entry, $nest = ⟨value⟩$ , $s = ⟨value⟩$ , $m = ⟨value⟩$ .
Constraint: $nest \geq m + 4$ when $s = 0.0$ .
NE_SPLINE_COEFF_CONV: The iterative process has failed to converge. Possibly s is too small: $s = ⟨value⟩$ .
NE_WEIGHTS_NOT_POSITIVE: On entry, the weights are not strictly positive: $weights [⟨value⟩] = ⟨value⟩$ .

If the function fails with an error exit of NE_SPLINE_COEFF_CONV or NE_NUM_KNOTS_1D_GT, a spline approximation is returned, but it fails to satisfy the fitting criterion (see (2) and (3)) – perhaps by only a small amount, however.

7 Accuracy

On successful exit, the approximation returned is such that its weighted sum of squared residuals

θ

(as in (3)) is equal to the smoothing factor

S

, up to a specified relative tolerance of

0.001

– except that if

n = 8

θ

may be significantly less than

S

: in this case the computed spline is simply a weighted least squares polynomial approximation of degree

3

, i.e., a spline with no interior knots.

8 Parallelism and Performance

Not applicable.

9 Further Comments

9.1 Timing

The time taken for a call of nag_1d_spline_fit (e02bec) depends on the complexity of the shape of the data, the value of the smoothing factor

S

, and the number of data points. If nag_1d_spline_fit (e02bec) is to be called for different values of

S

, much time can be saved by setting

start = Nag_Warm

after the first call.

9.2 Choice of S

If the weights have been correctly chosen (see Section 2.1.2 in the e02 Chapter Introduction), the standard deviation of

w_{r} y_{r}

would be the same for all

r

, equal to

σ

, say. In this case, choosing the smoothing factor

S

in the range

σ^{2} (m \pm \sqrt{2 m})

, as suggested by Reinsch (1967), is likely to give a good start in the search for a satisfactory value. Otherwise, experimenting with different values of

S

will be required from the start, taking account of the remarks in Section 3.

In that case, in view of computation time and memory requirements, it is recommended to start with a very large value for

S

and so determine the least squares cubic polynomial; the value returned in fp, call it

θ_{0}

, gives an upper bound for

S

. Then progressively decrease the value of

S

to obtain closer fits – say by a factor of

10

in the beginning, i.e.,

S = θ_{0} / 10

S = θ_{0} / 100

, and so on, and more carefully as the approximation shows more details.

The number of knots of the spline returned, and their location, generally depend on the value of

S

and on the behaviour of the function underlying the data. However, if nag_1d_spline_fit (e02bec) is called with

start = Nag_Warm

, the knots returned may also depend on the smoothing factors of the previous calls. Therefore if, after a number of trials with different values of

S

and

start = Nag_Warm

, a fit can finally be accepted as satisfactory, it may be worthwhile to call nag_1d_spline_fit (e02bec) once more with the selected value for

S

but now using

start = Nag_Cold

. Often, nag_1d_spline_fit (e02bec) then returns an approximation with the same quality of fit but with fewer knots, which is therefore better if data reduction is also important.

9.3 Outline of Method Used

S = 0

, the requisite number of knots is known in advance, i.e.,

n = m + 4

; the interior knots are located immediately as

λ_{i} = x_{i - 2}

, for

i = 5, 6, \dots, n - 4

. The corresponding least squares spline (see nag_1d_spline_fit_knots (e02bac)) is then an interpolating spline and therefore a solution of the problem.

S > 0

, a suitable knot set is built up in stages (starting with no interior knots in the case of a cold start but with the knot set found in a previous call if a warm start is chosen). At each stage, a spline is fitted to the data by least squares (see nag_1d_spline_fit_knots (e02bac)) and

θ

, the weighted sum of squares of residuals, is computed. If

θ > S

, new knots are added to the knot set to reduce

θ

at the next stage. The new knots are located in intervals where the fit is particularly poor, their number depending on the value of

S

and on the progress made so far in reducing

θ

. Sooner or later, we find that

θ \leq S

and at that point the knot set is accepted. The function then goes on to compute the (unique) spline which has this knot set and which satisfies the full fitting criterion specified by (2) and (3). The theoretical solution has

θ = S

. The function computes the spline by an iterative scheme which is ended when

θ = S

within a relative tolerance of

0.001

. The main part of each iteration consists of a linear least squares computation of special form, done in a similarly stable and efficient manner as in nag_1d_spline_fit_knots (e02bac).

An exception occurs when the function finds at the start that, even with no interior knots

(n = 8)

, the least squares spline already has its weighted sum of squares of residuals

\leq S

. In this case, since this spline (which is simply a cubic polynomial) also has an optimal value for the smoothness measure

η

, namely zero, it is returned at once as the (trivial) solution. It will usually mean that

S

has been chosen too large.

For further details of the algorithm and its use, see Dierckx (1981).

9.4 Evaluation of Computed Spline

The value of the computed spline at a given value x may be obtained in the double variable s by the call:

nag_1d_spline_evaluate(x, &s, &spline, &fail)

where spline is a structure of type Nag_Spline which is the output argument of nag_1d_spline_fit (e02bec).

The values of the spline and its first three derivatives at a given value x may be obtained in the array s of dimension at least 4 by the call:

nag_1d_spline_deriv(derivs, x, s, &spline, &fail)

where, if

derivs = Nag_LeftDerivs

, left-hand derivatives are computed and, if

derivs = Nag_RightDerivs

, right-hand derivatives are calculated. The value of derivs is only relevant if x is an interior knot (see nag_1d_spline_deriv (e02bcc)).

The value of the definite integral of the spline over the interval

x [0]

x [m - 1]

can be obtained in the variable integral by the call:

nag_1d_spline_intg(&spline, &integral, &fail)

see nag_1d_spline_intg (e02bdc).

10 Example

This example reads in a set of data values, followed by a set of values of

s

. For each value of

s

it calls nag_1d_spline_fit (e02bec) to compute a spline approximation, and prints the values of the knots and the B-spline coefficients

c_{i}

The program includes code to evaluate the computed splines, by calls to nag_1d_spline_evaluate (e02bbc), at the points

x_{r}

and at points mid-way between them. These values are not printed out, however; instead the results are illustrated by plots of the computed splines, together with the data points (indicated by

\times

) and the positions of the knots (indicated by vertical lines): the effect of decreasing

s

can be clearly seen.

NAG Library Function Documentnag_1d_spline_fit (e02bec)

+− Contents

1 Purpose

2 Specification

3 Description

4 References

5 Arguments

6 Error Indicators and Warnings

7 Accuracy

8 Parallelism and Performance

9 Further Comments

9.1 Timing

9.2 Choice of S

9.3 Outline of Method Used

9.4 Evaluation of Computed Spline

10 Example

10.1 Program Text

10.2 Program Data

10.3 Program Results

NAG Library Function Document

nag_1d_spline_fit (e02bec)