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Orthogonal Linear Regression in 3D-space by using Principal Components Analysis 1.0

  Date Added: May 23, 2013  |  Visits: 232

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

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Orthogonal Linear Regression in 3D-space by using Principal Components AnalysisThis 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.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 spaceExample:>> 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 ToolboxWe sincerely thank Peter Perkins, the author of the demo, and John D'Errico for their comments.

Requirements: No special requirements
Platforms: Matlab
Keyword: 039on039039off039 Direction Distances Error Figure Normal Plane Point Return Vector
Users rating: 0/10

License: Shareware Size: 10 KB
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