ó áp7]c@sTdZddlZddlmZdefd„ƒYZdedddd„Z dS(sM Created on Fri Dec 19 11:29:18 2014 Author: Josef Perktold License: BSD-3 iÿÿÿÿN(tstatstPredictionResultscBsVeZdddd„Zed„ƒZed„ƒZedd„Zddd„Z RS(cCs||_||_||_||_||_|dksE|dkr]tj|_d|_ n<|dkr‡tj |_|jf|_ n||_d|_ dS(Ntnormtt((( tpredicted_meant var_pred_meantdft var_residt row_labelstNoneRRtdistt dist_argsR(tselfRRRRR R((sAlib/python2.7/site-packages/statsmodels/regression/_prediction.pyt__init__s          cCstj|j|jƒS(N(tnptsqrtRR(R ((sAlib/python2.7/site-packages/statsmodels/regression/_prediction.pytse_obs#scCstj|jƒS(N(RRR(R ((sAlib/python2.7/site-packages/statsmodels/regression/_prediction.pytse_mean'sgš™™™™™©?cCsm|r|jn|j}|jjd|d|jŒ}|j||}|j||}tj||fƒS(s/ Returns the confidence interval of the value, `effect` of the constraint. This is currently only available for t and z tests. Parameters ---------- alpha : float, optional The significance level for the confidence interval. ie., The default `alpha` = .05 returns a 95% confidence interval. Returns ------- ci : ndarray, (k_constraints, 2) The array has the lower and the upper limit of the confidence interval in the columns. ig@(RRR tppfR RRt column_stack(R tobstalphatsetqtlowertupper((sAlib/python2.7/site-packages/statsmodels/regression/_prediction.pytconf_int+s  tallc Csddl}ddlm}|jd|dtƒ}|jd|dtƒ}|ƒ}|j|d<|j|d<|dd…df|d<|dd…d f|d <|dd…df|d <|dd…d f|d <||_|j |d |j d|j ƒƒ}|S(Niÿÿÿÿ(t OrderedDictRRtmeantmean_seit mean_ci_lowerit mean_ci_uppert obs_ci_lowert obs_ci_uppertindextcolumns( tpandast collectionsRRtTruetFalseRRttablet DataFrameRtkeys( R twhatRtpdRtci_obstci_meant to_includetres((sAlib/python2.7/site-packages/statsmodels/regression/_prediction.pyt summary_frameGs     N( t__name__t __module__R R tpropertyRRR(RR2(((sAlib/python2.7/site-packages/statsmodels/regression/_prediction.pyRs c Csž|rOt|jdƒrO|dk rOddlm}||jjj|ƒ}n|dk r|dkr‘t|ddƒ}t|ƒr‘d}q‘nt j |ƒ}|j dkrö|jj j dksÝ|jj j ddkrö|dd…df}nt j|ƒ}nW|jj }|dkr8t|jddƒ}n|dkr_t|jjddƒ}n|dk rÄt j |ƒ}|jdkrÄ|j dks²|j d |j dkrÄtd ƒ‚qÄn|dkrÙi}n|jj|j||}|jƒ}|t j||jƒjjdƒ} |j} |jd krM|jd } n|dk rf| |:} nd dg|j} t|| | d|jd| d|ƒS(sQ compute prediction results Parameters ---------- exog : array-like, optional The values for which you want to predict. transform : bool, optional If the model was fit via a formula, do you want to pass exog through the formula. Default is True. E.g., if you fit a model y ~ log(x1) + log(x2), and transform is True, then you can pass a data structure that contains x1 and x2 in their original form. Otherwise, you'd need to log the data first. weights : array_like, optional Weights interpreted as in WLS, used for the variance of the predicted residual. args, kwargs : Some models can take additional arguments or keywords, see the predict method of the model for the details. Returns ------- prediction_results : linear_model.PredictionResults The prediction results instance contains prediction and prediction variance and can on demand calculate confidence intervals and summary tables for the prediction of the mean and of new observations. tformulaiÿÿÿÿ(tdmatrixR#iNtweightsRisweights has wrong shapes fixed scaletscaleRRRR (thasattrtmodelR tpatsyR7tdatat design_infotgetattrtcallableRtasarraytndimtexogtshapet atleast_2dtsizet ValueErrortpredicttparamst cov_paramstdottTtsumR9tcov_typetcov_kwdstuse_tRtdf_resid( R RCt transformR8Rt pred_kwdsR7RtcovbRRR ((sAlib/python2.7/site-packages/statsmodels/regression/_prediction.pytget_prediction_sJ $    $    )   %   ( t__doc__tnumpyRtscipyRtobjectRR R'RU(((sAlib/python2.7/site-packages/statsmodels/regression/_prediction.pyts  P