ó áp7]c@s4ddlmZddlZddlmZddlmZddl m Z ddl m Z m Z ddlmZdefd „ƒYZd efd „ƒYZdd „Zd efd„ƒYZd„Zd„Zddddd„Zdefd„ƒYZdd„Zdddd„Zddded„ZdS(iÿÿÿÿ(trangeN(tf(tt(tstats(tclean0tfullrank(t multipleteststContrastResultscBskeZdZdddddddd„Zdd„Zd„Zd„Zdddd„Zddd„Z RS( sY Class for results of tests of linear restrictions on coefficients in a model. This class functions mainly as a container for `t_test`, `f_test` and `wald_test` for the parameters of a model. The attributes depend on the statistical test and are either based on the normal, the t, the F or the chisquare distribution. gš™™™™™©?c Ksü||_|dk rxd|_||_|j|_||_||_t|_||f|_ tj |||ƒ|_ nk|dk rîd|_||_ ||_||_ ||_t|_|f|_ |jj tj|ƒ|ƒd|_ nõd|krã|d|_|d|_|d|_ } ||_ tt|jƒ|_|jddƒ|_ |jdkrŽ|jj |j|ƒ|_ ||_qãtj| tjƒ|_ tj| ƒ} |jj tj| | ƒƒd|j | R s9s%N(R RR Rtlentstatsmodels.iolib.summaryR5Rt atleast_1dRRR R-thasattrtreprRRR( R!R7R"R9R8R6tiiR5tpvaluestsumm((s9lib/python2.7/site-packages/statsmodels/stats/contrast.pyR.fs4     /  c CsÎ|jd k r¾|jdk}d}|d kr_gtt|jƒƒD]}d|^qF}nddlm}|||j|j|j|j |j |ƒfd|d|d|d |ƒ}|St d ƒ‚d S( spReturn the parameter table as a pandas DataFrame This is only available for t and normal tests RR4sc%diÿÿÿÿ(tsummary_params_frameR6R7R8R"sonly available for t and zN( R RR RR:R;RBRR RR-R)(R!R7R"R8R6R?RBRA((s9lib/python2.7/site-packages/statsmodels/stats/contrast.pyt summary_frame¢s /   N( t__name__t __module__t__doc__RR&R-R/R2R.RC(((s9lib/python2.7/site-packages/statsmodels/stats/contrast.pyR s ,   >> import statsmodels.api as sm >>> from statsmodels.stats.contrast import Contrast >>> import numpy as np >>> np.random.seed(54321) >>> X = np.random.standard_normal((40,10)) # Get a contrast >>> new_term = np.column_stack((X[:,0], X[:,2])) >>> c = Contrast(new_term, X) >>> test = [[1] + [0]*9, [0]*2 + [1] + [0]*7] >>> np.allclose(c.contrast_matrix, test) True Get another contrast >>> P = np.dot(X, np.linalg.pinv(X)) >>> resid = np.identity(40) - P >>> noise = np.dot(resid,np.random.standard_normal((40,5))) >>> new_term2 = np.column_stack((noise,X[:,2])) >>> c2 = Contrast(new_term2, X) >>> print(c2.contrast_matrix) [ -1.26424750e-16 8.59467391e-17 1.56384718e-01 -2.60875560e-17 -7.77260726e-17 -8.41929574e-18 -7.36359622e-17 -1.39760860e-16 1.82976904e-16 -3.75277947e-18] Get another contrast >>> zero = np.zeros((40,)) >>> new_term3 = np.column_stack((zero,X[:,2])) >>> c3 = Contrast(new_term3, X) >>> test2 = [0]*2 + [1] + [0]*7 >>> np.allclose(c3.contrast_matrix, test2) True cCs#t|dƒs|jƒn|jS(s3 Gets the contrast_matrix property t_contrast_matrix(R=tcompute_matrixRH(R!((s9lib/python2.7/site-packages/statsmodels/stats/contrast.pyt _get_matrixüs cCs(tj|ƒ|_tj|ƒ|_dS(N(Rtasarrayttermtdesign(R!RLRM((s9lib/python2.7/site-packages/statsmodels/stats/contrast.pyR&scCs’|j}|jdkr1|dd…df}nt|ƒ|_|j|_t|j|jƒ|_y|j j d|_ Wnd|_ nXdS(sµ 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=design. iN( RLtndimRRtTRMtDtcontrastfromcolsRHtmatrixtshapetrank(R!RO((s9lib/python2.7/site-packages/statsmodels/stats/contrast.pyRI s  (RDRERFRJtpropertytcontrast_matrixR&RI(((s9lib/python2.7/site-packages/statsmodels/stats/contrast.pyRGºs A   cCsdtj|ƒ}tj|ƒ}|j\}}|jd|krb|jd|krbtdƒ‚n|dkrƒtjj|ƒ}n|jd|kr®tj||ƒj}n*|}tj|tj||jƒƒj}tj||jƒ}t |jƒdkr|df|_ntjj |ƒ|jdkrWt |ƒ}tj||ƒj}ntj |ƒS(sš 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). Parameters ---------- L : array-like D : array-like iisshape of L and D mismatchedN( RRKRSt ValueErrorRtlinalgtpinvtdotROR:t matrix_rankRR (tLRPtpseudotntptCtLp((s9lib/python2.7/site-packages/statsmodels/stats/contrast.pyRQ s$& $ tWaldTestResultscBsAeZddd„Zed„ƒZd„Zd„Zd„ZRS(cCs/||_||_||_||_|dk r…|dj|_|dj|_|dj|_|jdkr+|dj|_q+n¦|jdkr³t j |_ |jd|_n?|jdkræt j |_ |j\|_|_n t dƒ‚|dkr"|j jtj|ƒ|Œ|_n ||_dS( NR Rt df_constraintRRR is)only F and chi2 are possible distribution(ttableR R R RtvaluesR@tdf_constraintsRRR RRRWRRR(R!R R R RdR@((s9lib/python2.7/site-packages/statsmodels/stats/contrast.pyR&_s(         $cCsBd|j}|j|dg}|jdkr>|jdƒn|S(s'column names for summary table sP>%ss df constraintRsdf denom(R tappend(R!tpr_testt col_names((s9lib/python2.7/site-packages/statsmodels/stats/contrast.pyRis  cCsSt|dƒr|jStt|jj|jƒƒ}|jjd|ƒ|_|jS(Nt_dframetcolumns( R=RjtdicttzipRdRkRitrenametdframe(R!trenaming((s9lib/python2.7/site-packages/statsmodels/stats/contrast.pyRCŒs cCs|jƒjƒS(N(RCt to_string(R!((s9lib/python2.7/site-packages/statsmodels/stats/contrast.pyR/–scCst|jƒd|jƒS(Ns (R0R1R/(R!((s9lib/python2.7/site-packages/statsmodels/stats/contrast.pyR2šsN( RDRERR&RURiRCR/R2(((s9lib/python2.7/site-packages/statsmodels/stats/contrast.pyRb\s  ! cCsOtj|dƒ}gt|ŒD]&}d||d||df^q}|S(s8helper function for labels for pairwise comparisons is%s-%si(Rt triu_indicesRm(tk_levelt level_namest idx_pairs_alltnametlabels((s9lib/python2.7/site-packages/statsmodels/stats/contrast.pyt_get_pairs_labels¡s6c Csë|d}tj|dƒ}t|dƒ}tj||fƒ}d|tj|ƒ|dfs( ¯f <E  +  & # .