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Re: GLM fit or Cubic smoothing spline for categorical boundary data??
Thank you for the helpful replies. Based on some of the suggestions I tried a two parameter logistic curve fit using lsqcurvefit(). The equation used was y(t)=1/(1+exp(-r(t-t0))). The results obtained for the same data is attached. I have a few more questions:
1. Will the 4 parameter fit be better? And should I use y(t)= k1/(1+exp(-r(t-t0)))+k2 ?
2. Trueutwein and Strasberger (1999) suggest that maximum likelihood is better for fitting psychometric function data. Has anyone found results from a maximum likelihood fit better than a least squares fit?
Any opinions on this?
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