ó įp7]c@sldZddlZddlmZdefd„ƒYZdedddddd„Z dddd„Z dS(sM Created on Fri Dec 19 11:29:18 2014 Author: Josef Perktold License: BSD-3 i’’’’N(tstatstPredictionResultscBs}eZd d d d d d d„Zed„ƒZed„ƒZed„ƒZddd„Zddd „Z d dd „Z RS( c CsÆ||_||_||_||_||_||_||_|dksW|dkrotj |_ d|_ n<|dkr™tj |_ |jf|_ n||_ d|_ dS(Ntnormtt((( tpredicted_meant var_pred_meantdft var_residt row_labelstlinpredtlinktNoneRRtdistt dist_argsR( tselfRRRRR RR R ((s=lib/python2.7/site-packages/statsmodels/genmod/_prediction.pyt__init__s            cCst‚tj|j|jƒS(N(tNotImplementedErrortnptsqrtRR(R((s=lib/python2.7/site-packages/statsmodels/genmod/_prediction.pytse_obs&scCstj|jƒS(N(RRR(R((s=lib/python2.7/site-packages/statsmodels/genmod/_prediction.pytse_mean+scCs|j|jS(N(RR(R((s=lib/python2.7/site-packages/statsmodels/genmod/_prediction.pyttvalues/sis two-sidedcCs¬|j||j}|d krH|jjtj|ƒ|jŒd}nZ|d kro|jj||jŒ}n3|d kr–|jj||jŒ}n td ƒ‚||fS( s3z- or t-test for hypothesis that mean is equal to value Parameters ---------- value : array_like value under the null hypothesis alternative : string 'two-sided', 'larger', 'smaller' Returns ------- stat : ndarray test statistic pvalue : ndarray p-value of the hypothesis test, the distribution is given by the attribute of the instance, specified in `__init__`. Default if not specified is the normal distribution. s two-sideds2-sidedt2sitlargertltsmallertssinvalid alternative(s two-sideds2-sidedR(RR(RR( RRR tsfRtabsR tcdft ValueError(Rtvaluet alternativetstattpvalue((s=lib/python2.7/site-packages/statsmodels/genmod/_prediction.pytt_test3s (   tendpointgš™™™™™©?c Ksļtjdddƒ}|jj|ƒ|kjƒ}|dkrv| rv|jjd|dtƒ}|jj|ƒ}nu|dksˆ|rė|j}|j j d|d|j Œ} |j | |} |j | |} tj | | fƒ}n|S( 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. kwds : extra keyword arguments currently ignored, only for compatibility, consistent signature Returns ------- ci : ndarray, (k_constraints, 2) The array has the lower and the upper limit of the confidence interval in the columns. iiiR$talphatobstdeltag@(RtlinspaceR tinversetallR tconf_inttFalseRR tppfR Rt column_stack( RtmethodR%tkwdsttmpt is_lineart ci_lineartcitsetqtlowertupper((s=lib/python2.7/site-packages/statsmodels/genmod/_prediction.pyR+Ts  R*cCs¶ddl}ddlm}|jd|ƒ}|ƒ}|j|d<|j|d<|dd…df|d<|dd…d f|d <||_|j|d |jd |j ƒƒ}|S( s Summary framei’’’’N(t OrderedDictR%tmeantmean_seit mean_ci_lowerit mean_ci_uppertindextcolumns( tpandast collectionsR9R+RRttablet DataFrameRtkeys(RtwhatR%tpdR9tci_meant to_includetres((s=lib/python2.7/site-packages/statsmodels/genmod/_prediction.pyt summary_frame{s     N( t__name__t __module__R RtpropertyRRRR#R+RJ(((s=lib/python2.7/site-packages/statsmodels/genmod/_prediction.pyRs!'cCsĀ|rOt|jdƒrO|dk rOddlm}||jjj|ƒ}n|dk r|dkr‘t|ddƒ}t|ƒr‘d}q‘nt j |ƒ}|j dkrö|jj j dksŻ|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‚qÄnt|d <|jj|j||} |jƒ} |jjjj|jƒ} | d |t j| |jƒjjdƒ} |j} |jd kre|jd} n|dk r~| |:} nddg|j}t | | | d|j!d|d|d|d|ƒS(s^ 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 : generalized_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. tformulai’’’’(tdmatrixR>iNtweightsRisweights has wrong shapetlinearis fixed scaletscaleRRRR R R ("thasattrtmodelR tpatsyROtdatat design_infotgetattrtcallableRtasarraytndimtexogtshapet atleast_2dtsizeRR,tpredicttparamst cov_paramstfamilyR t inverse_derivRtdottTtsumRRtcov_typetcov_kwdstuse_tRtdf_resid(RR\t transformRPRR R t pred_kwdsRORtcovbt link_derivRRR ((s=lib/python2.7/site-packages/statsmodels/genmod/_prediction.pytget_prediction_glm’sL"$     $    )  -   c Csddlm}|d kr7|d kr7|jƒ}n|d kr^t|dƒr^|j}ntj|ƒ}|j|ƒ}|j |ƒ}|dtj |ƒ}t j } t |tj |ƒd| d|d|jƒƒ} t ||d| d|d| d|ƒ} | S( sJ results for univariate, nonlinear, monotonicaly transformed parameters This provides transformed values, standard errors and confidence interval for transformations of parameters, for example in calculating rates with `exp(params)` in the case of Poisson or other models with exponential mean function. i’’’’(tlinksR>iR RR R N(tstatsmodels.genmod.familiesRqR tLogRSR>RRZR)RdtdiagRRRtidentity( RaRbR RlRRqRRoRR R RI((s=lib/python2.7/site-packages/statsmodels/genmod/_prediction.pytparams_transform_univariateģs   ( t__doc__tnumpyRtscipyRtobjectRR tTrueRpRv(((s=lib/python2.7/site-packages/statsmodels/genmod/_prediction.pyts ƒ   X