Subject: Re: GLM fit or Cubic smoothing spline for categorical boundary data?? From: Pragati Rao <pragatir@xxxxxxxx> Date: Mon, 7 May 2012 15:53:21 +0530 List-Archive:<http://lists.mcgill.ca/scripts/wa.exe?LIST=AUDITORY>--14dae9340c4fd0a63504bf6fa8e0 Content-Type: multipart/alternative; boundary=14dae9340c4fd0a63104bf6fa8de --14dae9340c4fd0a63104bf6fa8de Content-Type: text/plain; charset=ISO-8859-1 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? Regards, Pragati --14dae9340c4fd0a63104bf6fa8de Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Hi everyone,<br><br>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)=3D1/(1+exp(-r(t-t0))). The results obtained fo= r the same data is attached. I have a few more questions:<br> <br>1. Will the 4 parameter fit be better? And should I use=A0 y(t)=3D k1/(= 1+exp(-r(t-t0)))+k2 ? <br><br>2. Trueutwein and Strasberger (1999) suggest = that maximum likelihood is better for fitting psychometric function data. H= as anyone found results from a maximum likelihood fit better than a least s= quares fit? <br> <br>Any opinions on this?<br><br>Regards,<br>Pragati<br> --14dae9340c4fd0a63104bf6fa8de-- --14dae9340c4fd0a63504bf6fa8e0 Content-Type: image/png; name="F2_hin_lsqcurv.png" Content-Disposition: attachment; filename="F2_hin_lsqcurv.png" Content-Transfer-Encoding: base64 X-Attachment-Id: f_h1xdrhb50 iVBORw0KGgoAAAANSUhEUgAABVYAAAKSCAIAAAC2hRdpAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA B3RJTUUH3AUHCTsxnqIVsAAAACJ0RVh0Q3JlYXRpb24gVGltZQAwNy1NYXktMjAxMiAxNToyOTo0 N6iMeWkAAAAkdEVYdFNvZnR3YXJlAE1BVExBQiwgVGhlIE1hdGh3b3JrcywgSW5jLrrEUs8AACAA SURBVHic7N3RcqO4FgVQmOr/TvLlzIMdBwPGgAU6ktaqrns7GdqBoI3gIEQ/DEMHAAAA1O6/3CsA AAAAXEEJAAAAAJqgBAAAAABNUAIAAACAJigBAAAAQBOUAAAAAKAJSgAAAADQBCUAAAAAaIISAAAA ADRBCQAAAACaoAQAAAAATVACAAAAgCYoAQAAAEATlAAAAACgCUoAAAAA0AQlAAAAAGiCEgAAAAA0 QQkAAAAAmqAEAAAAAE1QAgAAAIAmKAEAAABAE5QAAAAAoAlKAAAAANAEJQAAAABoghIAAAAANEEJ 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