tools.tools.isestimable()

statsmodels.tools.tools.isestimable

statsmodels.tools.tools.isestimable(C, D) [source]

True if (Q, P) contrast C is estimable for (N, P) design D

From an Q x P contrast matrix C and an N x P design matrix D, checks if the contrast C is estimable by looking at the rank of vstack([C,D]) and verifying it is the same as the rank of D.

Parameters:	C : (Q, P) array-like contrast matrix. If C has is 1 dimensional assume shape (1, P) D: (N, P) array-like design matrix
Returns:	tf : bool True if the contrast C is estimable on design D

Examples

>>> D = np.array([[1, 1, 1, 0, 0, 0],
...               [0, 0, 0, 1, 1, 1],
...               [1, 1, 1, 1, 1, 1]]).T
>>> isestimable([1, 0, 0], D)
False
>>> isestimable([1, -1, 0], D)
True