Using multiple dependent variables in fit function

I used the curve fitter app to generate a custom fit function with 9 dependent variables. When I run it I only get one goodness of fit (gof). How do I get a goodness of fit for each 9 variables I fit in the function?

Thanks for any help!!

function [fitresult, gof] = createFit(wavenumber, frame1)
%% Fit: 'after_cycle1_-1.25V_frame1'.
[xData, yData] = prepareCurveData( wavenumber, frame1 );

% Set up fittype and options.
ft = fittype( ['intensity_NR*exp(-((frequency_NR)/bandwidth_NR)^2)+...' ...
    'intensity_LF*exp(-((x-frequency_LF)/bandwidth_LF)^2)+...' ...
    'intensity_HF/(1+((frequency_HF-x)/bandwidth_HF)^2)'],...
    'independent', 'x', 'dependent', 'y' );
excludedPoints = (xData < 1900) | (xData > 2175);
opts = fitoptions( 'Method', 'NonlinearLeastSquares' );
opts.Display = 'Off';
opts.StartPoint = [15 150 100 2080 2055 2030 1300 150 70];
opts.Exclude = excludedPoints;

% Fit model to data.
[fitresult, gof] = fit( xData, yData, ft, opts );

Answer 1

[Manikanta Aditya]

Check this:

function [fitresult, gof, paramTests] = createFit(wavenumber, frame1)

%% Fit: 'after_cycle1_-1.25V_frame1'.
[xData, yData] = prepareCurveData(wavenumber, frame1);

% Set up fittype and options.
ft = fittype(['intensity_NR*exp(-((frequency_NR)/bandwidth_NR)^2)+...' ...
    'intensity_LF*exp(-((x-frequency_LF)/bandwidth_LF)^2)+...' ...
    'intensity_HF/(1+((frequency_HF-x)/bandwidth_HF)^2)'], ...
    'independent', 'x', 'dependent', 'y');

excludedPoints = (xData < 1900) | (xData > 2175);
opts = fitoptions('Method', 'NonlinearLeastSquares');
opts.Display = 'Off';
opts.StartPoint = [15 150 100 2080 2055 2030 1300 150 70];
opts.Exclude = excludedPoints;

% Fit model to data.
[fitresult, gof] = fit(xData, yData, ft, opts);

% Extract parameter estimates and confidence intervals
paramEstimates = fitresult.b;
paramCIs = confint(fitresult);

% Perform hypothesis testing on each parameter
alpha = 0.05; % Significance level
numParams = length(paramEstimates);
paramTests = cell(numParams, 1);

for i = 1:numParams
    paramValue = paramEstimates(i);
    paramCI = paramCIs(:, i);
    
    % Perform t-test (assuming normal distribution)
    tStat = paramValue / sqrt(fitresult.covb(i, i));
    pValue = 2 * tcdf(-abs(tStat), fitresult.dfe);
    
    % Store the test result
    paramTests{i} = struct('Estimate', paramValue, ...
                           'ConfidenceInterval', paramCI, ...
                           'tStatistic', tStat, ...
                           'pValue', pValue);
end
end

[Jaclyn Rebstock]

This worked beautifully. Thanks!!

[Manikanta Aditya]

Great to know @Jaclyn Rebstock, if you found answer helpful you can accept it so that others can refer it if needed.