On Tue, Jan 19, 2010 at 14:47, Gael Varoquaux <[email protected]> wrote: > On Tue, Jan 19, 2010 at 02:22:30PM -0600, Robert Kern wrote: >> > y = y - np.dot(confounds.T, linalg.lstsq(confounds.T, y)[0]) > >> > with np = numpy and linalg = scipy.linalg where scipy calls ATLAS. > >> For clarification, are you trying to find the components of the y >> vectors that are perpendicular to the space spanned by the 10 >> orthonormal vectors in confounds? > > Yes. Actually, what I am doing is calculating partial correlation between > x and y conditionally to confounds, with the following code: > > def cond_partial_cor(y, x, confounds=[]): > """ Returns the partial correlation of y and x, conditionning on > confounds. > """ > # First orthogonalise y and x relative to confounds > if len(confounds): > y = y - np.dot(confounds.T, linalg.lstsq(confounds.T, y)[0]) > x = x - np.dot(confounds.T, linalg.lstsq(confounds.T, x)[0]) > return np.dot(x, y)/sqrt(np.dot(y, y)*np.dot(x, x)) > > I am not sure that what I am doing is optimal.
If confounds is orthonormal, then there is no need to use lstsq(). y = y - np.dot(np.dot(confounds, y), confounds) -- Robert Kern "I have come to believe that the whole world is an enigma, a harmless enigma that is made terrible by our own mad attempt to interpret it as though it had an underlying truth." -- Umberto Eco _______________________________________________ NumPy-Discussion mailing list [email protected] http://mail.scipy.org/mailman/listinfo/numpy-discussion
