The logistic regression implies a model of process (it looks likeâSâ function, saturated on infinities etc). Hence, you can extrapolate your results, if you need. But in a case of spline fit you can predict reasonably only in the range your measurements were done.
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.
All India Institute of Speech and Hearing,