Goodness of Fit (Modified)

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

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