model fit comparison matlab
3 views (last 30 days)
Show older comments
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
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
0 Comments
See Also
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!