On an earlier post, I described my first (rather weak) attempt at logistic regression. It failed because I did not structure the problem properly. I was trying to see which channels contributed best to recognition using each channel's classification rate. Of course, this is exactly what the algorithm gives back. In order to use logisitic regression in our prolbem, we need to use it to give back not the classification rate of each channel, but the probability that the trial is a particular class given the results of each channel. I've italicized results because we can either look at the output of our least squares or correlation coefficient classification, or, we can look at a more basic level at the waveforms themselves. I will start with the former. Besides binary responses, logisitic regression can be used to classify both nominal and ordinal responses. Nominal responses are unordered, e.g., French, Italian, or Thousand Island. Ordinal responses are ordered, e.g., no pain, slightly painful, or really painful. In our particular application the classification classes are nominal. This type of analysis is known as nomial, multinomial or polytomous logisitic regression.
In SAS, PROC LOGISTIC is used for ordinal logistic regression while PROC CATMOD is used for nominal logistic regression. I will be using the latter. Fortunately, SAS gives some examples on how to use this in command line mode. An even better tutorial can be found at Queens. Unfortunately, I could discern no way of using this command using Analyst. Incidentally, I found a quick reference on file handling in SAS. Be careful not to erase your file. The DIRECT keyword should not be used, as it specifies that the data should be treated as quantitative rather than qualititive. In this case the classification gives the class which is qualititative.
The data should be formatted like so:
| Ch1 | Ch2 | Ch3 | Ch4 | Ch5 | Truth |
| 1 | 5 | 4 | 3 | 5 | 6 |