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Goodness of Fit (Modified)

4.8 | 5 ratings Rate this file 13 Downloads (last 30 days) File Size: 3.04 KB File ID: #22020 Version: 1.8

Goodness of Fit (Modified)



07 Nov 2008 (Updated )

Computes goodness of fit for regression model given matrix/vector of target and output values.

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 GFIT2 Computes goodness of fit for regression model

       [gf] = gfit2(t,y)
       [gf] = gfit2(t,y,gFitMeasure)
       [gf] = gfit2(t,y,gFitMeasure,options)

           t: matrix or vector of target values for regression model
           y: matrix or vector of output from regression model.
 gFitMeasure: a string or cell array of string values representing
               different form of goodness of fit measure as follows:
               'all' - calculates all the measures below
               '1' - mean squared error (mse)
               '2' - normalised mean squared error (nmse)
               '3' - root mean squared error (rmse)
               '4' - normalised root mean squared error (nrmse)
               '5' - mean absolute error (mae)
               '6' - mean absolute relative error (mare)
               '7' - coefficient of correlation (r)
               '8' - coefficient of determination (d)
               '9' - coefficient of efficiency (e)
               '10' - maximum absolute error
               '11' - maximum absolute relative error

    options: a string containing other output options, currently the only option is verbose output.

                'v' - verbose output, posts some text output for the
                    chosen measures to the command line

      gf: vector of goodness of fit values between model output and target for each of the strings in gFitMeasure


      gf = gfit2(t,y); for all statistics in list returned as vector

      gf = gfit2(t,y,'3'); for root mean squared error

      gf = gfit2(t,y, {'3'}); for root mean squared error

      gf = gfit2(t,y, {'1' '3' '9'}); for mean squared error, root mean
            | squared error, and coefficient of
           \|/ efficiency
      gf = [mse rmse e]

      gf = gfit2(t,y,'all','v'); for all statistics in list returned as
                    vector with information posted to the
                    command line on each statistic

      gf = gfit2(t,y, {'1' '3' '9'}, 'v'); for mean squared error, root
                    mean squared error, and
                    coefficient of efficiency as a
                    vector with information on each
                    of these also posted to the
                    command line


Goodness Of Fit inspired this file.

This file inspired Gapolyfitn.

MATLAB release MATLAB 7.2 (R2006a)
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Comments and Ratings (5)
04 Aug 2016 antrian gokil

24 Aug 2014 Ricardo

Very useful and well written function. Thanks.

08 Mar 2010 Andre Guy Tranquille

27 Oct 2009 Ben

Ben (view profile)

24 Nov 2008 David Yeo

Thank you. !!

07 Nov 2008 1.1

Fixed some bugs and spelling mistakes in comments, and added new output option.

21 Nov 2008 1.2

Changed output for gfit2(t,y) now returns all available statistics as a vector rather than just the mean squared error (choice 1). Also made minor changes to comments and help section.

24 Nov 2008 1.7

Actually uploaded new file this time, managed to forget to on last update.

01 Jul 2009 1.8

Added a verbose option to return some information to the command line

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