# model fit comparison matlab

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Ilias Minas on 30 May 2022
Edited: Hiro on 31 May 2022
Hi all,
I have the following graph which shows the raw data of an experiment and the predicted data after executing 2 models.
I would like to compare which model fits better in the raw data.
I tried to use compare and goodnesofffit but the results didnt make sense.
Is there any alternative to this?
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Hiro on 30 May 2022
Did you measure the goodness of fit like this?:
mse1 = mean((raw-oneState).^2)

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### Answers (1)

Mathieu NOE on 30 May 2022
hello
this is basically waht is called the R² correlation coefficient . If it get's close to 1 it's a good fit, if it get's close to zero there is no fit .
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function Rsquared = my_Rsquared_coeff(data,data_fit)
% R2 correlation coefficient computation
% The total sum of squares
sum_of_squares = sum((data-mean(data)).^2);
% The sum of squares of residuals, also called the residual sum of squares:
sum_of_squares_of_residuals = sum((data-data_fit).^2);
% definition of the coefficient of correlation is
Rsquared = 1 - sum_of_squares_of_residuals/sum_of_squares;
end
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