Source code for statsmodels.tsa.base.prediction

import numpy as np
import pandas as pd
from scipy import stats

[docs] class PredictionResults: """ 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`` """ def __init__( self, predicted_mean, var_pred_mean, dist=None, df=None, row_labels=None, ): self._predicted_mean = np.asarray(predicted_mean) self._var_pred_mean = np.asarray(var_pred_mean) self._df = df self._row_labels = row_labels if row_labels is None: self._row_labels = getattr(predicted_mean, "index", None) self._use_pandas = self._row_labels is not None if dist != "t" and df is not None: raise ValueError('df must be None when dist is not "t"') if dist is None or dist == "norm": self.dist = stats.norm self.dist_args = () elif dist == "t": self.dist = stats.t self.dist_args = (self._df,) elif isinstance(dist, stats.distributions.rv_frozen): self.dist = dist self.dist_args = () else: raise ValueError('dist must be a None, "norm", "t" or a callable.') def _wrap_pandas(self, value, name=None, columns=None): if not self._use_pandas: return value if value.ndim == 1: return pd.Series(value, index=self._row_labels, name=name) return pd.DataFrame(value, index=self._row_labels, columns=columns) @property def row_labels(self): """The row labels used in pandas-types.""" return self._row_labels @property def predicted_mean(self): """The predicted mean""" return self._wrap_pandas(self._predicted_mean, "predicted_mean") @property def var_pred_mean(self): """The variance of the predicted mean""" if self._var_pred_mean.ndim > 2: return self._var_pred_mean return self._wrap_pandas(self._var_pred_mean, "var_pred_mean") @property def se_mean(self): """The standard deviation of the predicted mean""" ndim = self._var_pred_mean.ndim if ndim == 1: values = np.sqrt(self._var_pred_mean) elif ndim == 3: values = np.sqrt(self._var_pred_mean.T.diagonal()) else: raise NotImplementedError("var_pre_mean must be 1 or 3 dim") return self._wrap_pandas(values, "mean_se") @property def tvalues(self): """The ratio of the predicted mean to its standard deviation""" val = self.predicted_mean / self.se_mean if isinstance(val, pd.Series): = "tvalues" return val
[docs] def t_test(self, value=0, alternative="two-sided"): """ 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. """ # assumes symmetric distribution stat = (self.predicted_mean - value) / self.se_mean if alternative in ["two-sided", "2-sided", "2s"]: pvalue = self.dist.sf(np.abs(stat), *self.dist_args) * 2 elif alternative in ["larger", "l"]: pvalue = self.dist.sf(stat, *self.dist_args) elif alternative in ["smaller", "s"]: pvalue = self.dist.cdf(stat, *self.dist_args) else: raise ValueError("invalid alternative") return stat, pvalue
[docs] def conf_int(self, alpha=0.05): """ 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. """ se = self.se_mean q = self.dist.ppf(1 - alpha / 2.0, *self.dist_args) lower = self.predicted_mean - q * se upper = self.predicted_mean + q * se ci = np.column_stack((lower, upper)) if self._use_pandas: return self._wrap_pandas(ci, columns=["lower", "upper"]) return ci
[docs] def summary_frame(self, alpha=0.05): """ 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. """ ci_mean = np.asarray(self.conf_int(alpha=alpha)) lower, upper = ci_mean[:, 0], ci_mean[:, 1] to_include = { "mean": self.predicted_mean, "mean_se": self.se_mean, "mean_ci_lower": lower, "mean_ci_upper": upper, } return pd.DataFrame(to_include)

Last update: Dec 14, 2023