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

Hi all,

I have identification responses for stimuli along F2 and VOT continuums from a group of subjects. I tried fitting glmfit() with logistic regression in MATLAB because each subject had to choose either 'pa' or 'ta' for F2 and 'ta' or 'da' for VOT task. However, in many subjects I noticed that the curve leaves out many data points and gives absurd values for categorical boundaries. So I tried fitting cubic spline using csaps(). As expected the curve fits very well. I have attached png files of results obtained through both techniques. Can we use cubic smoothing spline on such a data set?

I do not have a strong base in statistics. Any help will be greatly appreciated.

Pragati Rao
Research Officer,
All India Institute of Speech and Hearing,
Mysore, India.

Attachment: F2_continuum_hin_GLM.png
Description: PNG image

Attachment: F2_continuum_hin_Spline.png
Description: PNG image