o 0Gf @s.ddlZddlZddlmZGdddZdS)N)statsc@seZdZdZ   dddZdddZeddZed d Zed d Z ed dZ eddZ dddZ dddZ dddZdS)PredictionResultsa Prediction results Parameters ---------- predicted_mean : {ndarray, Series, DataFrame} The predicted mean values var_pred_mean : {ndarray, Series, DataFrame} The variance of the predicted mean values dist : {None, "norm", "t", rv_frozen} The distribution to use when constructing prediction intervals. Default is normal. df : int, optional The degree of freedom parameter for the t. Not used if dist is None, "norm" or a callable. row_labels : {Sequence[Hashable], pd.Index} Row labels to use for the summary frame. If None, attempts to read the index of ``predicted_mean`` NcCst||_t||_||_||_|durt|dd|_|jdu|_|dkr/|dur/td|dus7|dkr@t j |_ d|_ dS|dkrOt j |_ |jf|_ dSt|t jjr^||_ d|_ dStd)Nindextz$df must be None when dist is not "t"normz/dist must be a None, "norm", "t" or a callable.)npasarray_predicted_mean_var_pred_meanZ_df _row_labelsgetattr _use_pandas ValueErrorrrdist dist_argsr isinstanceZ distributionsZ rv_frozen)selfpredicted_mean var_pred_meanrZdf row_labelsrr?lib/python3.10/site-packages/statsmodels/tsa/base/prediction.py__init__s&     zPredictionResults.__init__cCs8|js|S|jdkrtj||j|dStj||j|dS)N)rname)rcolumns)rndimpdSeriesr DataFrame)rvaluerrrrr _wrap_pandas:s  zPredictionResults._wrap_pandascCs|jS)z$The row labels used in pandas-types.)r rrrrrAszPredictionResults.row_labelscCs||jdS)zThe predicted meanr)r!r r"rrrrFsz PredictionResults.predicted_meancCs |jjdkr |jS||jdS)z"The variance of the predicted meanr)r rr!r"rrrrKs zPredictionResults.var_pred_meancCsN|jj}|dkrt|j}n|dkrt|jj}ntd||dS)z,The standard deviation of the predicted meanrzvar_pre_mean must be 1 or 3 dimmean_se)r rrZsqrtTZdiagonalNotImplementedErrorr!)rrvaluesrrrse_meanRs zPredictionResults.se_meancCs"|j|j}t|tjrd|_|S)z9The ratio of the predicted mean to its standard deviationtvalues)rr)rrrr)rvalrrrr*^s  zPredictionResults.tvaluesr two-sidedcCs|j||j}|dvr |jjt|g|jRd}||fS|dvr3|jj|g|jR}||fS|dvrF|jj|g|jR}||fStd)a8 z- or t-test for hypothesis that mean is equal to value Parameters ---------- value : array_like value under the null hypothesis alternative : str '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. )r,z2-sidedZ2sr#)Zlargerl)Zsmallerszinvalid alternative) rr)rZsfrabsrZcdfr)rr Z alternativestatZpvaluerrrt_testfs zPredictionResults.t_test皙?cCsj|j}|jjd|dg|jR}|j||}|j||}t||f}|jr3|j|ddgdS|S)a  Confidence interval construction for the predicted mean. This is currently only available for t and z tests. Parameters ---------- alpha : float, optional The significance level for the prediction interval. The default `alpha` = .05 returns a 95% confidence interval. Returns ------- pi : {ndarray, DataFrame} The array has the lower and the upper limit of the prediction interval in the columns. rg@lowerupper)r) r)rZppfrrrZ column_stackrr!)ralphaZseqr3r4Zcirrrconf_intszPredictionResults.conf_intcCsPt|j|d}|dddf|dddf}}|j|j||d}t|S)a Summary frame of mean, variance and confidence interval. Returns ------- DataFrame DataFrame containing four columns: * mean * mean_se * mean_ci_lower * mean_ci_upper Notes ----- Fixes alpha to 0.05 so that the confidence interval should have 95% coverage. )r5Nrr)Zmeanr%Z mean_ci_lowerZ mean_ci_upper)rr r7rr)rr)rr5Zci_meanr3r4Z to_includerrr summary_frames" zPredictionResults.summary_frame)NNN)NN)rr,)r2)__name__ __module__ __qualname____doc__rr!propertyrrrr)r*r1r7r8rrrrrs(        !r)ZnumpyrZpandasrZscipyrrrrrrs