'covariance matrix of estimate
# not sure it\'s correct, need to check orientation everywhere
# looks ok, display needs getting used to
>>> v.rss[None,None,:]*np.linalg.inv(np.dot(v.xred.T,v.xred))[:,:,None]
array([[[ 0.37247445,  0.32210609],
[ 0.1002642 ,  0.08670584]],
[[ 0.1002642 ,  0.08670584],
[ 0.45903637,  0.39696255]]])
>>> v.rss[0]*np.linalg.inv(np.dot(v.xred.T,v.xred))
array([[ 0.37247445,  0.1002642 ],
[ 0.1002642 ,  0.45903637]])
>>> v.rss[1]*np.linalg.inv(np.dot(v.xred.T,v.xred))
array([[ 0.32210609,  0.08670584],
[ 0.08670584,  0.39696255]])'

def covmat(self):

    self.paramcov = (self.rss[None, None, :] * np.linalg.inv(np.dot(self.xred.T, self.xred))[:, :, None])
