Hi J.A., The Great Thief of a Thread! ;) I am sorry for our scarce docstring of zscore :-/
pervoxel=False is actually what you are looking for I believe -- row-wise standartization. just be careful with it -- using it for generalization assessment is ok, but features are not 'voxels' anylonger and you shouldn't look at their sensitivities from then. On Tue, 30 Nov 2010, J.A. Etzel wrote: > Setting perchunk=False and pervoxel=True normalizes the entire > column: each voxel has a mean of zero and standard deviation of one > over all volumes in the dataset. > Setting perchunk=False and pervoxel=False normalizes over all > columns: all voxels together have a mean of zero and standard > deviation of one over all volumes in the dataset. > Setting perchunk=True and pervoxel=False normalizes over all columns > within each chunk: all voxels together have a mean of zero and > standard deviation of one over all volumes in each chunk. > This gives various options for normalizing column-wise, but is it > possible to normalize row-wise? In other words, normalize so that > the voxels in each sample (volume) have a mean of zero and standard > deviation one? Chunks are irrelevant for this case, since each > sample is normalized separately. If this is already in pyMVPA, can > you point me in the right direction? -- =------------------------------------------------------------------= Keep in touch www.onerussian.com Yaroslav Halchenko www.ohloh.net/accounts/yarikoptic _______________________________________________ Pkg-ExpPsy-PyMVPA mailing list Pkg-ExpPsy-PyMVPA@lists.alioth.debian.org http://lists.alioth.debian.org/mailman/listinfo/pkg-exppsy-pymvpa
