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"
which is Copyright by the MathWorks, Inc.
- 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'
- 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
>> 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]';
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.
Ivo Petras, Igor Podlubny, May 2006
An example of application can be found at:
For additional codes for the Orthogonal Linear Regression also known as Total Least Squares Method see link:
Could you update your link in the description to: https://www.mathworks.com/examples/statistics/mw/stats_featured-ex25288136-fitting-an-orthogonal-regression-using-principal-components-analysis?
Updated description. Added screenshot.
Fixing the misunderstanding around the reuse of some pieces of the code from Peter Perkins's demo.
Updated description, spelling correction.
Some minor errors correction.