adcmfit.m

function [cf_ gof_] = adcmfit(tm,ADCM,ADC)
%   Number of data sets:  1
%   Number of fits:  1

% Data from data set "ADCM vs. tm":
%     X = tm:
%     Y = ADCM:
%     Unweighted

 tm = tm(:);
 ADCM = ADCM(:);



% --- Create fit ---                                        %on if robust fitting
fo_ = fitoptions('method','NonlinearLeastSquares','Robust','Off','Lower',[0 0],'Upper',[5e-2 1],'DiffMinChange',1.0000000000000000623e-09,'TolFun',9.9999999999999995475e-08,'TolX',9.9999999999999995475e-08);
ok_ = isfinite(tm) & isfinite(ADCM);
if ~all( ok_ )
    warning( 'GenerateMFile:IgnoringNansAndInfs',...
        'Ignoring NaNs and Infs in data.' );
end
st_ = [0.0010000000000000000416 0.80000000000000004441 ];
set(fo_,'Startpoint',st_);
ft_ = fittype([num2str(ADC) '*(1-sigma*exp(-AXR*tm))'],...
    'dependent',{'ADCM'},'independent',{'tm'},...
    'coefficients',{'AXR', 'sigma'});

% Fit this model using new data
[cf_ gof_] = fit(tm(ok_),ADCM(ok_),ft_,fo_);
% Alternatively uncomment the following lines to use coefficients from the
% original fit. You can use this choice to plot the original fit against new
% data.
%    cv_ = { 0.003801454702637618345, 0.15012008612495458815};
%    cf_ = cfit(ft_,cv_{:});