model fit comparison matlab

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?

[Hiro]

Did you measure the goodness of fit like this?:

mse1 = mean((raw-oneState).^2)

Answer 1

[Mathieu NOE]

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