# calculate goodness of fit

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I have a data set which looks like this

I have around 7 equations like

y=atp*kcat*Et.*x ./ (kd_A*km_g + km_g*atp + km_a*x + atp*x.*(1+x./ksi));

y=atp*kcat*E.*x ./ (km_g*atp + km_a*x.*(1+x./ksi) + atp*x);

and more...

There are 4 - 5 parameters to fit for in each equation and there are 3 - 4 known variables.

One of these equations should be the best fit for the data and I want to find that. I tried using lsqcurvefit and got pretty good results. But similar values fpr the variables using all the equations and the resnorm is identical. Is there a way to find the goodness of fit? And am I doing it correct?

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### Accepted Answer

Star Strider
on 20 Feb 2014

Edited: Star Strider
on 20 Feb 2014

If you have the Statistics Toolbox, I suggest fitnlm. It produces a number of useful statistics. To the best of my knowledge, these aren’t available with lsqcurvefit and the Optimization Toolbox.

As a general rule, if all your functions produce similar residual errors, the model with the fewest parameters to estimate will have the best goodness-of-fit because it has the greatest degrees-of-freedom. The F-statistic may be the best measure, although the likelihood ratio may be best for for comparing two models with the same number of parameters.

That said, there are a number of other techniques to assess regression models. It is too broad a subject to go into here.

##### 1 Comment

Pitch Mandava
on 20 Mar 2014

Edited: Pitch Mandava
on 20 Mar 2014

### More Answers (2)

jandas
on 20 Feb 2014

Try to use the fit function

[fitobject,gof] = fit(x,y,fitType)

and have a look at the gof output argument

##### 0 Comments

Matt J
on 21 Feb 2014

Edited: Matt J
on 21 Feb 2014

##### 2 Comments

Matt J
on 21 Nov 2015

Edited: Matt J
on 27 Nov 2015

Hi Kate,

The usual formulas for goodness of fit do not apply to constrained problems. However, I assume your constraints are simple bounds LB<=x<=UB, since those are the only kind that lsqcurvefit supports. One can usually re-parametrize such problems as unconstrained e.g., by the change of variables x-->y,

x=(UB-LB)*(sin(y)+1)/2 - LB

Note that x, as given in terms of y above, is inherently bounded to [LB, UB] while y can vary freely.

I would recommend that you solve the problem first in terms of the natural variable x using lsqcurvefit and explicit bounds. Then, re-pose the problem as unconstrained and use nlinfit to obtain goodness of fit output. Because you already know the solution from lsqcurvefit, you can simply pass that solution as the beta0 input to nlinfit, and it will finish in 1 iteration or so, spitting out goodness of fit output with it. Of course, the goodness of fit will be in terms of the new unconstrained variables y. You will have to translate that through the change of variables formula to get things in terms of x.

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