On Tue, Jan 19, 2010 at 1:47 PM, 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. > > > > Most of the time is spent in linalg.lstsq. The length of the vectors is > > > 810, and there are about 10 confounds. > > > Exactly what are the shapes? y.shape = (810, N); confounds.shape = (810, > 10)? > > Sorry, I should have been more precise: > > y.shape = (810, ) > confounds.shape = (10, 810) > > Column stack the bunch so that the last column is y, then do a qr decomposition. The last column of q is the (normalized) orthogonal vector and its amplitude is the last (bottom right) component of r. Chuck
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