Specifying bin edges when fitting a normal distribution using histfit

Question

I want to fit a histogram to some data using predefined bins. All my data points are between 1 and 10, so I want the bins to start from xmin=1, and end at xmax=10, with a step of 0.5.

I use the following commands:

x = d1.data(:,4); % x is my data
H = histfit(x,10,'normal'); % fits a histogram using 10 bins 

However when doing the above, bins are determined automatically per dataset and do not correspond to the edges I want. How can I ensure that the same bin edges are used for all datasets?

Answer 1

If you have access to the Curve Fitting Toolbox, I would suggest another approach that provides the required flexibility. This involves doing the fit "yourself" instead of relying on histfit:

% Generate some data:
rng(66221105) % set random seed, for reproducibility
REAL_SIG = 1.95;
REAL_MU = 5.5;
X = randn(200,1)*REAL_SIG + REAL_MU;

% Define the bin edges you want
EDGES = 1:0.5:10;

% Bin the data according to the predefined edges:
Y = histcounts(X, EDGES);

% Fit a normal distribution using the curve fitting tool:
binCenters = conv(EDGES, [0.5, 0.5], 'valid'); % moving average
[xData, yData] = prepareCurveData( binCenters, Y );

ft = fittype( 'gauss1' );
fitresult = fit( xData, yData, ft );
disp(fitresult); % optional

% Plot fit with data (optional)
figure(); 
histogram(X, EDGES); hold on; grid on;
plot(fitresult); 

Which yields the following plot:

and the fitted model:

 General model Gauss1:
 fitresult(x) =  a1*exp(-((x-b1)/c1)^2)
 Coefficients (with 95% confidence bounds):
   a1 =       19.65  (17.62, 21.68)
   b1 =        5.15  (4.899, 5.401)
   c1 =       2.971  (2.595, 3.348)