First of all thank you for your help in advance. My question is regarding Matlab and non-linear Least Square curve fitting in Matlab - in both I am not familiar with.
I have this type of data:
x = [600, 800, 1000, 1200, 1400];
y = [0, 02, 04, 0.7, 1];
I am trying to use the following algorithms:
f = @(p,x) (p(3)-p(4))./(1+exp(-(x-p(2))/p(1)))+p(4);
opts = optimset('Display','off','MaxFunEvals',1000);
sigfit = lsqcurvefit(f, starting_value, intervals,problong,,,opts);
The only problem I have is the starting value in sigfit variable. What would be the best starting values given the above numbers? Any help please would be extremely appreciated.
The above algorithms are based on lsqcurvefit function found in Matlab. Here is the link: http://uk.mathworks.com/help/optim/ug/lsqcurvefit.html
The X vector is time intervals in milliseconds, whereas the Y vector represents responses some participant made whether those intervals where perceived as close to a short (400ms) or long (1600ms) interval.
I don't understand what starting points mean. Ultimately what I need to do is find the 0.5 point in the Y axis and the corresponding value on the X axis. The solution will be somewhere between 600ms to 1400 ms and probably around the 1200ms mark. I have put the starting value as a vector from 600 to 1400 but, I have no idea whether that is right or what that means. I was hoping someone better equipped than me can help answer this problem precisely :).
Thank you again,