[pymvpa] Linear SVM classifiers

Hi all, Could anyone tell me if there's a tangible difference between using the LinearNuSVMC() and LinearCSVMC() classifiers? Also, in order to have the classifier choose its own best fit, is it best practice to leave the area between the brackets blank, or to but in a value of C=-1 ? I think

Re: [pymvpa] effect size (in lieu of zscore)

sorry for being silent On Tue, 03 Jan 2012, Mike E. Klein wrote: (1) I haven't done a permutation test. By chance distribution I just meant the bulk of the data points using my real-label-coded data. While I'm obviously hoping for a histogram that contains a positive skew, at

Re: [pymvpa] effect size (in lieu of zscore)

On 1/4/2012 3:20 PM, Mike E. Klein wrote: I have toyed with a bit of ROI MVPA: found some accuracies that were above-chance, though I'm not sure if they were convincingly so. You're suggesting that it should run an analysis with permuted labels on, for example A1 and another area, and then look

Re: [pymvpa] effect size (in lieu of zscore)

I had similar feeling -- performance distributions should be pretty much a mixture of two: chance distribution (centered at chance level for that task) and some interesting one in the right tail, e.g. as we have shown in a toy example in