Is it possible to fit multiple data sets in the same 'fit'??

So my dilema is that the code below runs the first fit until 'DiffMinChange' is satisfied and then it moves to the second fit. I want to know if it is possible to operate both fits and then check 'DiffMinChange'. I assume I will need a while statement, but I am not sure how the 'fit' function will react. Any help is appreciated!

% Define first fittype
ft1 = fittype('FitLT1(xdata1, mtrlmod, cgs, cds, cgd0, coxd, vtd, vtdtco, fc, m, wb, nb, a, agd, thetal, thetalr, thetah, thetaltexp, thetahtexp, kfl, kfh, kpl, kph, kflr, kplr, kfltexp, kfhtexp, kpltexp, kphtexp, vtl, vth, vtlr, vtltco, vthtco, vbigd, pvfl, pvfh, slmin, id0, vb, rs, rd, rdr, rdvd, rdvg11, rdvg12, rdtemp1, rdtemp2, rdvdtemp1, rdvdtemp2, rdiode, is_body, kvsg1, kvsg2, nd, temperature, tnom, vk1, vk2, tt, tau, multiplier, p_delta, thetalrtexp, kplrtexp, rdrtemp1, rdrtemp2, rdiodetemp1, rdiodetemp2, vtlrtco, vk1tco, vk2tco, ndtco, kvsg1texp, kvsg2texp)',...
             'independent', {'xdata1'},...
             'dependent', {'ydata1'},...
             'problem', optInfo1.const_vars,...
             'coefficients', optInfo1.opt_params);
         
% Call first fitting function and save the optimized parameter values,
% measurements on goodness of fit, and optimization data.
[fit_result1, gof1, fit_info1] = fit(xdata1, ydata1, ft1, 'problem', optInfo1.const_vals,... 
                               'Lower', optInfo1.lower_bound, 'Upper', optInfo1.upper_bound, 'DiffMinChange', optInfo1.min_diff);

                           
                           
% Define second fittype                          
ft2 = fittype('FitLT2(xdata2, mtrlmod, cgs, cds, cgd0, coxd, vtd, vtdtco, fc, m, wb, nb, a, agd, thetal, thetalr, thetah, thetaltexp, thetahtexp, kfl, kfh, kpl, kph, kflr, kplr, kfltexp, kfhtexp, kpltexp, kphtexp, vtl, vth, vtlr, vtltco, vthtco, vbigd, pvfl, pvfh, slmin, id0, vb, rs, rd, rdr, rdvd, rdvg11, rdvg12, rdtemp1, rdtemp2, rdvdtemp1, rdvdtemp2, rdiode, is_body, kvsg1, kvsg2, nd, temperature, tnom, vk1, vk2, tt, tau, multiplier, p_delta, thetalrtexp, kplrtexp, rdrtemp1, rdrtemp2, rdiodetemp1, rdiodetemp2, vtlrtco, vk1tco, vk2tco, ndtco, kvsg1texp, kvsg2texp)',...
             'independent', {'xdata2'},...
             'dependent', {'ydata2'},...
             'problem', optInfo2.const_vars,...
             'coefficients', optInfo2.opt_params);

% Call second fitting function and save the optimized parameter values,
% measurements on goodness of fit, and optimization data.
[fit_result2, gof2, fit_info2] = fit(xdata2, ydata2, ft2, 'problem', optInfo2.const_vals,... 
                               'Lower', optInfo2.lower_bound, 'Upper', optInfo2.upper_bound, 'DiffMinChange', optInfo2.min_diff);

Answer 1

[KALASH]

Hi Steven,

I understand that you want to calculate the value of the variable “diffMinChange” after fitting both the datasets.

You can use the while loop implementation as shown below:

% Set initial values for loop control 
diffMinChangeCondition1 = false;  
diffMinChangeCondition2 = false;  

% Define first fittype 
ft1 = fittype(...); % Your first fittype definition 
    
% Define second fittype 
ft2 = fittype(...); % Your second fittype definition 
    
% Loop until 'DiffMinChange' is satisfied for both fits 
while ~ (diffMinChangeCondition1 && diffMinChangeCondition2)  
    
    % Call first fitting function and save the optimized parameter values, 
    [fit_result1, gof1, fit_info1] = fit(xdata1, ydata1, ft1, 'problem', optInfo1.const_vals,...  
        'Lower', optInfo1.lower_bound, 'Upper', optInfo1.upper_bound, 'DiffMinChange', optInfo1.min_diff); 
    
    % Call second fitting function and save the optimized parameter values, 
    [fit_result2, gof2, fit_info2] = fit(xdata2, ydata2, ft2, 'problem', optInfo2.const_vals,...  
        'Lower', optInfo2.lower_bound, 'Upper', optInfo2.upper_bound, 'DiffMinChange', optInfo2.min_diff); 
    
    % Check if DiffMinChange condition is satisfied for both fits 
    diffMinChangeCondition1 = fit_info1.DiffMinChange < optInfo1.min_diff; 
    diffMinChangeCondition2 = fit_info2.DiffMinChange < optInfo2.min_diff; 
end 

This is one of the ways of doing it. Feel free to modify the code according to your needs. Hope it helps!