B ¤ŠãZ÷ã@sVddlZddlZddlmZddlmZGdd„deƒZ Gdd„deƒZ d dd „Z dS) éN)Úpinv)Ú utils_oldc@s*eZdZdZd dd„Zdd„Zdd„ZdS) ÚContrastResultsz¸ Results from looking at a particular contrast of coefficients in a parametric model. The class does nothing, it is a container for the results from T and F contrasts. NcCs8|dk r||_||_||_n||_||_||_||_dS)N)ÚFÚdf_denomÚdf_numÚtÚsdÚeffect)Úselfrrr r rr©r ú?lib/python3.7/site-packages/statsmodels/sandbox/contrast_old.pyÚ__init__szContrastResults.__init__cCst|dƒr|jS|jSdS)Nr)Úhasattrrr)r r r r Ú __array__s zContrastResults.__array__cCsJt|dƒr"dt|jƒ|j|jfSdt|jƒt|jƒt|jƒ|jfSdS)Nrz*z1)rÚreprrrrr r r)r r r r Ú__str__ s  zContrastResults.__str__)NNNNNN)Ú__name__Ú __module__Ú __qualname__Ú__doc__rrrr r r r rs  rc@s:eZdZdZd dd„Zdd„Zdd„Zd d „ZeeƒZ d S) ÚContrastah This class is used to construct contrast matrices in regression models. They are specified by a (term, formula) pair. The term, T, is a linear combination of columns of the design matrix D=formula(). The matrix attribute is a contrast matrix C so that colspan(dot(D, C)) = colspan(dot(D, dot(pinv(D), T))) where pinv(D) is the generalized inverse of D. Further, the matrix Tnew = dot(C, D) is full rank. The rank attribute is the rank of dot(D, dot(pinv(D), T)) In a regression model, the contrast tests that E(dot(Tnew, Y)) = 0 for each column of Tnew. ÚcCs*||_||_|dkr t|ƒ|_n||_dS)Nr)ÚtermÚformulaÚstrÚname)r rrrr r r rAs  zContrast.__init__cCsdtt|jƒt|jƒdœƒS)Nz )rr)rrrr)r r r r rIszContrast.__str__cOsœt |j¡}|jj|_t t |||Ž¡¡}|jdkrF|jddf|_t   |¡|_ |jj ||Ž|_ t|j |j ƒ|_y|jjd|_Wnd|_YnXdS)a  Construct a contrast matrix C so that colspan(dot(D, C)) = colspan(dot(D, dot(pinv(D), T))) where pinv(D) is the generalized inverse of D=self.D=self.formula(). If the design, self.D is already set, then evaldesign can be set to False. érN)ÚcopyrrÚ namespaceÚnpZ transposeZarrayÚndimÚshapeÚutilsZclean0ÚTZdesignÚDÚcontrastfromcolsÚ_matrixÚmatrixÚrank)r ÚargsÚkwrr$r r r Úcompute_matrixMs    zContrast.compute_matrixcCst|dƒs| ¡|jS)z` This will fail if the formula needs arguments to construct the design. r')rr,r')r r r r Ú _get_matrixjs zContrast._get_matrixN)r) rrrrrrr,r-Úpropertyr(r r r r r)s  rcCsèt |¡}t |¡}|j\}}|jd|krB|jd|krBtdƒ‚|dkrRt|ƒ}|jd|krpt ||¡j}n|}t |t ||j¡¡j}t ||j¡}t|jƒdkr²|df|_t  |¡|jdkrÞt  |¡}t ||¡j}t  |¡S)aV From an n x p design matrix D and a matrix L, tries to determine a p x q contrast matrix C which determines a contrast of full rank, i.e. the n x q matrix dot(transpose(C), pinv(D)) is full rank. L must satisfy either L.shape[0] == n or L.shape[1] == p. If L.shape[0] == n, then L is thought of as representing columns in the column space of D. If L.shape[1] == p, then L is thought of as what is known as a contrast matrix. In this case, this function returns an estimable contrast corresponding to the dot(D, L.T) Note that this always produces a meaningful contrast, not always with the intended properties because q is always non-zero unless L is identically 0. That is, it produces a contrast that spans the column space of L (after projection onto the column space of D). rrzshape of L and D mismatchedN) r Zasarrayr"Ú ValueErrorrÚdotr$Úlenr#r)ZfullrankZsqueeze)ÚLr%ZpseudoÚnÚpÚCZLpr r r r&ts$     r&)N) rZnumpyr Z numpy.linalgrZstatsmodels.sandboxrr#Úobjectrrr&r r r r Ús   "K