ó áp7]c@sáddlZddlmZddljjZdejfd„ƒYZ de fd„ƒYZ de fd„ƒYZ d e fd „ƒYZ d ej fd „ƒYZd ejfd„ƒYZejeeƒe Ze Ze ZdS(iÿÿÿÿN(tmodelt_DimReductionRegressioncBs eZdZd„Zd„ZRS(sB A base class for dimension reduction regression methods. cKs tt|ƒj|||dS(N(tsuperRt__init__(tselftendogtexogtkwargs((s<lib/python2.7/site-packages/statsmodels/regression/dimred.pyR scCs¨tj|jƒ}|j|dd…f}||jdƒ8}tj|jƒ}tjj|ƒ}tjj ||jƒj}||_ ||_ tj ||ƒ|_ dS(Ni(tnptargsortRRtmeantcovtTtlinalgtcholeskytsolvetwexogt_covxrt array_splitt _split_wexog(Rtn_slicetiitxtcovxtcovxr((s<lib/python2.7/site-packages/statsmodels/regression/dimred.pyt_preps  (t__name__t __module__t__doc__RR(((s<lib/python2.7/site-packages/statsmodels/regression/dimred.pyRs tSlicedInverseRegcBseZdZd„ZRS(s0 Sliced Inverse Regression (SIR) Parameters ---------- endog : array-like (1d) The dependent variable exog : array-like (2d) The covariates References ---------- KC Li (1991). Sliced inverse regression for dimension reduction. JASA 86, 316-342. c Ks3|jddƒ}|jjd|}|j|ƒg|jD]}|jdƒ^q=}g|jD]}|jd^qb}tj|ƒ}tj|ƒ}tj|j d|ƒ}tj j |ƒ\}} tj | ƒ} || }| dd…| f} tj j |jj | ƒ} t|| d|ƒ} t| ƒS(s Estimate the EDR space. Parameters ---------- slice_n : int, optional Number of observations per slice tslice_niitfweightsNteigs(tgetRtshapeRRR RtasarrayR R R teighR RRtDimReductionResultstDimReductionResultsWrapper( RRRRtztmntntmnctatbtjjtparamstresults((s<lib/python2.7/site-packages/statsmodels/regression/dimred.pytfit1s  %# (RRRR0(((s<lib/python2.7/site-packages/statsmodels/regression/dimred.pyR stPrincipalHessianDirectionscBseZdZd„ZRS(so Principal Hessian Directions Parameters ---------- endog : array-like (1d) The dependent variable exog : array-like (2d) The covariates References ---------- KC Li (1992). On Principal Hessian Directions for Data Visualization and Dimension Reduction: Another application of Stein's lemma. JASA 87:420. cKs7|jdtƒ}|j|jjƒ}|j|jjdƒ}|rxddlm}|||ƒjƒ}|j}nt j d|||ƒ}|t |ƒ:}t j |j ƒ}t jj||ƒ} t jj| ƒ\} } t jt j| ƒ ƒ} | | } | dd…| f} t|| d| ƒ}t|ƒS(s% Estimate the EDR space using PHD. Parameters ---------- resid : bool, optional If True, use least squares regression to remove the linear relationship between each covariate and the response, before conducting PHD. tresidiiÿÿÿÿ(tOLSs i,ij,ik->jkNR (R!tFalseRR Rt#statsmodels.regression.linear_modelR3R0R2RteinsumtlenR R R RteigR tabsR%R&(RRR2tyRR3trtcmtcxtcbR+R,R-R.R/((s<lib/python2.7/site-packages/statsmodels/regression/dimred.pyR0es"   (RRRR0(((s<lib/python2.7/site-packages/statsmodels/regression/dimred.pyR1SstSlicedAverageVarianceEstimationcBs eZdZd„Zd„ZRS(s\ Sliced Average Variance Estimation (SAVE) Parameters ---------- endog : array-like (1d) The dependent variable exog : array-like (2d) The covariates bc : bool, optional If True, use the bias-correctedCSAVE method of Li and Zhu. References ---------- RD Cook. SAVE: A method for dimension reduction and graphics in regression. http://www.stat.umn.edu/RegGraph/RecentDev/save.pdf Y Li, L-X Zhu (2007). Asymptotics for sliced average variance estimation. The Annals of Statistics. https://arxiv.org/pdf/0708.0462.pdf cKsQtt|ƒj|||t|_d|krM|dtkrMt|_ndS(Ntbc(RtSAVERR4R@tTrue(RRRR((s<lib/python2.7/site-packages/statsmodels/regression/dimred.pyR¢s cKsÝ|jddƒ}|jjd|}|j|ƒg|jD]}tj|jƒ^q=}g|jD]}|jd^qe}|jjd}|j sýd}xJt ||ƒD]9\} } tj |ƒ| } || tj | | ƒ7}q­W|t |ƒ:}n^d} x$|D]} | tj | | ƒ7} q W| t |ƒ:} d}x€|jD]u}||jdƒ}xYt|jdƒD]D}||dd…f}tj||ƒ}|tj ||ƒ7}qwWqJW||jjd:}tj|ƒ} | | d| ddd}| d| ddd}|| ||}tj |ƒdt|ƒt |ƒ|}tjj|ƒ\}}tj| ƒ}||}|dd…|f}tjj|jj|ƒ}t||d|ƒ}t|ƒS(s“ Estimate the EDR space. Parameters ---------- slice_n : int Number of observations per slice Ri2iiNiR (R!RR"RRRR R RR@tzipteyetdotR7R trangetoutertsumR R$R RRR%R&(RRRRR'tcvtnstptvmtwtcvxticvtavtctvnRR;titutmtk1tk2tav2R+R,R-R.R/((s<lib/python2.7/site-packages/statsmodels/regression/dimred.pyR0©sH  (#  + (RRRRR0(((s<lib/python2.7/site-packages/statsmodels/regression/dimred.pyR?Šs R%cBseZdZd„ZRS(s= Results class for a dimension reduction regression. cCs&tt|ƒj||ƒ||_dS(N(RR%RR (RRR.R ((s<lib/python2.7/site-packages/statsmodels/regression/dimred.pyRðs (RRRR(((s<lib/python2.7/site-packages/statsmodels/regression/dimred.pyR%ësR&cBseZidd6ZeZRS(tcolumnsR.(RRt_attrst _wrap_attrs(((s<lib/python2.7/site-packages/statsmodels/regression/dimred.pyR&ös (tnumpyRtstatsmodels.baseRtstatsmodels.base.wrappertbasetwrappertwraptModelRRR1R?tResultsR%tResultsWrapperR&tpopulate_wrappertSIRtPHDRA(((s<lib/python2.7/site-packages/statsmodels/regression/dimred.pyts 37a