Evaluate the "goodness of fit" of a distribution fitting, when using the function "fitdist"

When I use the function fitdist to find the best distribution fitting, I would like to measure or assess the goodness of fitting.

What are the possible ways to measure the goodness of fitting of a distribution fitting, when employing the function fitdist?

% Example where I would like to assess the goodness of fit for this
% histogram:
rng default; 
r = normrnd(10,10,1000,1);
histogram(r,'binwidth',1,'Normalization','pdf') % <-- my data
pd = fitdist(r,'normal');                       % <-- fit the distribution to the data (fitdist)
xgrid = linspace(-30,100,100)';
pdfEst = pdf(pd,xgrid);
line(xgrid,pdfEst,'Linewidth',2,'color','r')

[Sim]

To evaluate the gof of distributions fitting, do we have something similar to the curve fitting's gof in Matlab?

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

[Sim]

Anyone who could suggest something? :-)

[Ive J]

There are several approaches you may want to take a look at, such as Cramér–von Mises or Anderson-Darling test, or Cullen and Frey graph.

doc adtest 

also: https://se.mathworks.com/matlabcentral/fileexchange/50157-cramer-von-mises-test

Check also gofstat function in R package fitdistrplus to get more ideas on how you can conduct such GOF tests in MATLAB.

[Sim]

thanks a lot @Ive J!!! Why dont you put it as an answer... maybe with one Minimum Working Example (MWE)...?

[Mathieu NOE]

if you have access to both the histogram x,y data and your fit data , why not simply doing the R² computation as follows :

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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

[Sim]

Thanks a lot @Mathieu NOE, that's great! .....I did not think about the R2 for this case..... :-)

[Mathieu NOE]

my pleasure !