Matlab: Extracting error values from a fit - matlab

Question

I am using the following code to import a spreadsheet with two coloumns x = intensity and y = angle and to get the Gaussian curve fitting:
%% Initialize variables
filename = ['E:\XRD\Enamel\MPS\3PC\Chiplots (starting from 0)\MPS_3Pcontrol_map_' j '.dat'];
startRow = 5;
%% Format string for each line of text:
formatSpec = '%14f%f%[^\n\r]';
%% Open the text file.
fileID = fopen(filename,'r');
%% Read columns of data according to format string.
dataArray = textscan(fileID, formatSpec, 'Delimiter', '', 'WhiteSpace', '', 'HeaderLines' ,startRow-1, 'ReturnOnError', false);
%% Close the text file.
fclose(fileID);
%% Allocate imported array to column variable names
plots.angle = dataArray{:, 1};
plots.intensity = dataArray{:, 2};
%% Fit: 'Gauss'.
[xData, yData] = prepareCurveData(plots.(['angle' num2str(j)]), plots.(['intensity' num2str(j)]));
% Set up fittype and options.
ft = fittype( 'y0+a*exp(-((x-xa)/wa)^2) + b*exp(-((x-xb)/wb)^2) + c*exp(-((x-xc)/wc)^2) + d*exp(-((x-xd)/wd)^2) + e*exp(-((x-xe)/we)^2) + f*exp(-((x-xf)/wf)^2)', 'independent', 'x', 'dependent', 'y' );
opts = fitoptions( 'Method', 'NonlinearLeastSquares' );
opts.Display = 'Off';
opts.Lower = [0 0 0 0 0 0 0.1 0.1 0.1 0.1 0.1 0.1 -Inf -Inf -Inf -Inf -Inf -Inf -Inf];
opts.StartPoint = [1400 1400 1000 1200 1200 1000 25 25 25 25 25 25 75 125 170 250 275 325 0.5];
opts.Upper = [inf inf inf inf inf inf 50 50 50 50 50 50 inf inf inf inf inf inf Inf];
% Fit model to data.
[fitresult, gof] = fit( xData, yData, ft, opts );
I can get the values of the coefficients using: cfit(fitresult) to get the following:
From the above I can extract the coefficients values using coeffvalues(cfit(fitresult)):
The problem is I cant get the values highlighted in yellow, which I need to calculate the standard error using the equation (standard error = standard deviation/square root of n)
Can I extract the values highlighted in yellow in order to calculate the standard error?

Answer 1

You need to use the confint function. This will give you the 95% confidence bounds for the fit. It works like this:
confint(cfit(fitresult))
If you want your own confidence interval, you can set it like this:
confint(cfit(fitresult,[insert confidence interval here (such as 0.85)]))
That should do what you are looking for.