# Orthogonal Linear Regression in 3D-space by using Principal Components Analysis (Scripts) 1.0

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• Last update: 5 years ago
• Operating system: Linux, Mac OS X, Win All, BSD, Solaris
• Publisher: Ivo Petras (6 other programs)
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## Orthogonal Linear Regression in 3D-space by using Principal Components Analysis (Scripts) Publisher's description

### Orthogonal Linear Regression in 3D-space by using Principal Components Analysis

Orthogonal Linear Regression in 3D-space by using Principal Components Analysis

This is a wrapper function to some pieces of the code from the Statistics Toolbox demo titled "Fitting an Orthogonal Regression Using Principal Components Analysis"
(http://www.mathworks.com/products/statisti...thoregdemo.html),
which is Copyright by the MathWorks, Inc.

Input parameters:
- XData: input data block -- x: axis
- YData: input data block -- y: axis
- ZData: input data block -- z: axis
- geometry: type of approximation ('line','plane')
- visualization: figure ('on','off') -- default is 'on'
- sod: show orthogonal distances ('on','off') -- default is 'on'

Return parameters:
- Err: error of approximation - sum of orthogonal distances
- N: normal vector for plane, direction vector for line
- P: point on plane or line in 3D space

Example:

>> XD = [4.8 6.7 6.2 6.2 4.1 1.9 2.0]';
>> YD = [13.4 9.9 5.8 6.1 6.7 10.6 11.5]';
>> ZD = [13.7 13.1 11.3 11.8 12.5 16.2 18.5]';
>> fit_3D_data(XD,YD,ZD,'line','on','on');
>> fit_3D_data(XD,YD,ZD,'plane','on','off');

Note: Written for Matlab 7.0 (R14) with Statistics Toolbox

We sincerely thank Peter Perkins, the author of the demo, and John D'Errico for their comments.

#### System Requirements:

MATLAB 7 (R14)
Program Release Status: New Release
Program Install Support: Install and Uninstall

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