Finding the vertical offset of a gaussian fit

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Ahmed Abdulla
Ahmed Abdulla 2021-7-28
评论： Stefan Rick 2023-6-19
I apologize for posting this question again, as I believe I miss presented my question earlier.
I have the following data points as shown in the picture, and I am in the quest to obtain the vertical offset (v0) for the best gaussian fit to my data point. Equation for gaussian fit: gaus = amp*exp(-(((x-mu).^2)/(2*sig.^2)))+ v0.
I currently have the datasets x and y and I am stuck at that, any help is appreciated and sorry for posting this again
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Matt J
Matt J 2021-7-28
编辑：Matt J 2021-7-28
sorry for posting this again
Indeed, you Accepted an answer to the same question here:
https://www.mathworks.com/matlabcentral/answers/887569-obtaining-the-gaussian-equation-for-a-distribution?s_tid=srchtitle
Why do you need a different answer now?
Adam Danz
Adam Danz 2021-7-29
Also, the previous answer you accepted and then unaccepted also includes a vertical offset term and even lists the fit parameter values in the axes title.
You also copied the guassian equation from the previous answer into your current question above. I don't see how this question differs from the previous question.

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Matt J
Matt J 2021-7-28
编辑：Matt J 2021-7-28
Ran in:
This File Exchange routine does gaussian+constant fitting:
https://www.mathworks.com/matlabcentral/fileexchange/69116-gaussfitn
mu=5;
sig=2.3;
amp=4;
v0=1.7;
x=linspace(0,10,100);
y=amp*exp(-(((x-mu).^2)/(2*sig.^2)))+ v0;
y=y+randn(size(y))*0.1;
p=gaussfitn(x(:),y(:));
Local minimum possible. lsqcurvefit stopped because the final change in the sum of squares relative to its initial value is less than the value of the function tolerance.
[v0,amp,mu,var]=p{:}, sig=sqrt(var)
v0 = 1.6697
amp = 4.0421
mu = 4.9868
var = 5.4130
sig = 2.3266
yfit=v0+amp*exp(-(((x-mu).^2)/(2*var)));
plot(x,y,'o',x,yfit)
  2 个评论
Stefan Rick
Stefan Rick 2023-6-19
I try to test gaussfitn with my own data and get the error:
"Error using gaussfitn
Expected zdata to be an array with number of elements equal to 1."
for
p=gaussfitn(1:645,yData)
Anyone find the mistake?

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