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Re: GLM fit or Cubic smoothing spline for categorical boundary data??

Hi everyone,

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?


Attachment: F2_hin_lsqcurv.png
Description: PNG image