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Re: GLM fit or Cubic smoothing spline for categorical boundary data??
A disadvantage of splines is that the estimated boundary will be
strongly affected by data points close to the boundary, and independent
of the values at the extremes. Ideally you'd want to use all the data
points, weighted by the number of trials at each point.
Chapter and verse on how to fit PFs with maximum likelihoods can be
found in Treutwin & Strasburger, "Fitting the psychometric function",
Perception and Psychophysics, 1999 (61(1) 87-106.
On 04/05/2012 12:13, Pragati Rao wrote:
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
All India Institute of Speech and Hearing,
UCL Speech, Hearing & Phonetic Sciences