B3spline: Difference between revisions

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===Purpose===
===Purpose===


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===Description===
===Description===


Curve fitting using second order splines where yi = f(xi) for i=1,...,M.  
Curve fitting using second order splines where
 
yi = f(xi) for i=1,...,M.  


See (options.algorithm) for more information.
See (options.algorithm) for more information.
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* '''x''' = Mx1 vector of independent variable values.
* '''x''' = Mx1 vector of independent variable values.


* '''y''' = Mx1 vector of corresponding dependent variable values.
* '''y''' = Mx1 vector of corresponding dependendent variable values.
 
* '''t''' = defines the number of knots or knot positions.
 
*  ''''''= 1x1 scalar integer defining the number of uniformly distributed INTERIOR knots. There will be t+2 knots positioned at:
 
*  '''modl.t''' = linspace(min(x),max(x),t+2)';
 
*  ''''''= Kx1 vector defining manually placed knot positions,


* '''t''' = defines the number of knots or knot positions. This can be either:
* '''where''' modl.t = sort(t);
** A scalar integer defining the number of uniformly distributed INTERIOR knots. There will be t+2 knots positioned at: modl.t = linspace(min(x),max(x),t+2)';
** A Kx1 vector defining manually placed knot positions, where modl.t = sort(t);


Note that knot positions need not be uniform, and that t(1) can be <min(x) and t(K) can be >max(x).
Note that knot positions need not be uniform, and that t(1) can be <min(x) and t(K) can be >max(x).


However, knot positions must be such that there are at least 3 unique data points between each knot:  tk,tk+1 for k=1,...,K.
Note that knot positions must be such that there are at least 3 unique data points between each knot:  tk,tk+1 for k=1,...,K.


====OUTPUTS====
====OUTPUTS====
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* '''algorithm''': [ {'b3spline'} | 'b3_0' | 'b3_01' ] fitting algorithm
* '''algorithm''': [ {'b3spline'} | 'b3_0' | 'b3_01' ] fitting algorithm
** ''''b3_0'''': is the same as 'b3spline' but also constrains the ends to 0: f{1,2}(t1) = 0 and f{K-1,K}(tK) = 0.
 
** ''''b3_01'''' is 'b3_0' but also constrains the derivatives at the ends to 0: f'{1,2}(t1) = 0 and f'{K-1,K}(tK) = 0.
''' 'b3spline'''': fits quadradic polynomials f{k,k+1} to the data between knots tk, k=1,...,K, subject to:
** ''' 'b3spline'''': fits quadradic polynomials f{k,k+1} to the data between knots tk, k=1,...,K, subject to:
 
::  f{k,k+1}(tk+1)  = f{k+1,k+2}(tk+1) and
  f{k,k+1}(tk+1)  = f{k+1,k+2}(tk+1) and
::  f'{k,k+1}(tk+1) = f'{k+1,k+2}(tk+1) for k=1,...,K-1.
  f'{k,k+1}(tk+1) = f'{k+1,k+2}(tk+1) for k=1,...,K-1.
 
''''b3_0'''': is the same as 'b3spline' but also constrains the ends to 0: f{1,2}(t1) = 0 and f{K-1,K}(tK) = 0.
 
''''b3_01':''' is 'b3_0' but also constrains the derivatives at the ends to 0: f'{1,2}(t1) = 0 and f'{K-1,K}(tK) = 0.


* '''order''': positive integer for polynomial order {default = 1}.
* '''order''': positive integer for polynomial order {default = 1}.

Revision as of 15:24, 3 September 2008

Purpose

Univariate spline fit and prediction.

Synopsis

modl = b3spline(x,y,t,options);
pred = b3spline(x,modl,options);
valid = b3spline(x,y,modl,options);

Description

Curve fitting using second order splines where

yi = f(xi) for i=1,...,M.

See (options.algorithm) for more information.

INPUTS

  • x = Mx1 vector of independent variable values.
  • y = Mx1 vector of corresponding dependendent variable values.
  • t = defines the number of knots or knot positions.
  • '= 1x1 scalar integer defining the number of uniformly distributed INTERIOR knots. There will be t+2 knots positioned at:
  • modl.t = linspace(min(x),max(x),t+2)';
  • '= Kx1 vector defining manually placed knot positions,
  • where modl.t = sort(t);

Note that knot positions need not be uniform, and that t(1) can be <min(x) and t(K) can be >max(x).

Note that knot positions must be such that there are at least 3 unique data points between each knot: tk,tk+1 for k=1,...,K.

OUTPUTS

  • modl = standard model structure containing the spline model (See MODELSTRUCT).
  • pred = structure array with predictions.
  • valid = structure array with predictions.

Options

  • options = a structure array with the following fields:
  • display: [ {'on'} | 'off' ] level of display to command window.
  • plots: [ {'final'} | 'none' ] governs level of plotting. If 'final' and calibrating a model, the plot shows plot(xi,yi) and plot(xi,f(xi),'-') with knots.
  • algorithm: [ {'b3spline'} | 'b3_0' | 'b3_01' ] fitting algorithm

'b3spline': fits quadradic polynomials f{k,k+1} to the data between knots tk, k=1,...,K, subject to:

 f{k,k+1}(tk+1)  = f{k+1,k+2}(tk+1) and
 f'{k,k+1}(tk+1) = f'{k+1,k+2}(tk+1) for k=1,...,K-1.

'b3_0': is the same as 'b3spline' but also constrains the ends to 0: f{1,2}(t1) = 0 and f{K-1,K}(tK) = 0.

'b3_01': is 'b3_0' but also constrains the derivatives at the ends to 0: f'{1,2}(t1) = 0 and f'{K-1,K}(tK) = 0.

  • order: positive integer for polynomial order {default = 1}.

The default options can be retreived using: options = baseline('options');.

See Also