model fit comparison matlab

2 views (last 30 days)
Ilias Minas
Ilias Minas on 30 May 2022
Edited: Hiro Yoshino 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?
  1 Comment
Hiro Yoshino
Hiro Yoshino on 30 May 2022
Edited: Hiro Yoshino on 31 May 2022
Did you measure the goodness of fit like this?:
mse1 = mean((raw-oneState).^2)

Sign in to comment.

Answers (1)

Mathieu NOE
Mathieu NOE on 30 May 2022
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;


Find more on Curve Fitting Toolbox in Help Center and File Exchange

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!