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# Goodness of Fit (Modified) 1.0

Date Added: May 15, 2013  |  Visits: 195

GFIT2 Computes goodness of fit for regression model USAGE: [gf] = gfit2(t,y) [gf] = gfit2(t,y,gFitMeasure) [gf] = gfit2(t,y,gFitMeasure,options) INPUT: 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 OUTPUT: gf: vector of goodness of fit values between model output and target for each of the strings in gFitMeasure EXAMPLES 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

 Requirements: No special requirements Platforms: Matlab Keyword: Efficiency,  Maximum,  Options Users rating: 0/10