Source code for statsmodels.regression._prediction

Created on Fri Dec 19 11:29:18 2014

Author: Josef Perktold
License: BSD-3


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

# this is similar to ContrastResults after t_test, copied and adjusted
[docs] class PredictionResults: """ Results class for predictions. Parameters ---------- predicted_mean : ndarray The array containing the prediction means. var_pred_mean : ndarray The array of the variance of the prediction means. var_resid : ndarray The array of residual variances. df : int The degree of freedom used if dist is 't'. dist : {'norm', 't', object} Either a string for the normal or t distribution or another object that exposes a `ppf` method. row_labels : list[str] Row labels used in summary frame. """ def __init__(self, predicted_mean, var_pred_mean, var_resid, df=None, dist=None, row_labels=None): self.predicted = predicted_mean self.var_pred = var_pred_mean self.df = df self.var_resid = var_resid self.row_labels = row_labels 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,) else: self.dist = dist self.dist_args = () @property def se_obs(self): return np.sqrt(self.var_pred_mean + self.var_resid) @property def se_mean(self): return @property def predicted_mean(self): # alias for backwards compatibility return self.predicted @property def var_pred_mean(self): # alias for backwards compatibility return self.var_pred @property def se(self): # alias for backwards compatibility return np.sqrt(self.var_pred_mean)
[docs] def conf_int(self, obs=False, alpha=0.05): """ 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. """ se = self.se_obs if obs else self.se_mean q = self.dist.ppf(1 - alpha / 2., *self.dist_args) lower = self.predicted_mean - q * se upper = self.predicted_mean + q * se return np.column_stack((lower, upper))
[docs] def summary_frame(self, alpha=0.05): # TODO: finish and cleanup ci_obs = self.conf_int(alpha=alpha, obs=True) # need to split ci_mean = self.conf_int(alpha=alpha, obs=False) to_include = {} to_include['mean'] = self.predicted_mean to_include['mean_se'] = self.se_mean to_include['mean_ci_lower'] = ci_mean[:, 0] to_include['mean_ci_upper'] = ci_mean[:, 1] to_include['obs_ci_lower'] = ci_obs[:, 0] to_include['obs_ci_upper'] = ci_obs[:, 1] self.table = to_include # pandas dict does not handle 2d_array # data = np.column_stack(list(to_include.values())) # names = .... res = pd.DataFrame(to_include, index=self.row_labels, columns=to_include.keys()) return res
def get_prediction(self, exog=None, transform=True, weights=None, row_labels=None, pred_kwds=None): """ 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. row_labels : list A list of row labels to use. If not provided, read `exog` is available. **kwargs Some models can take additional keyword arguments, see the predict method of the model for the details. Returns ------- 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. """ # prepare exog and row_labels, based on base Results.predict if transform and hasattr(self.model, 'formula') and exog is not None: from patsy import dmatrix if isinstance(exog, pd.Series): # GH-6509 exog = pd.DataFrame(exog) exog = dmatrix(, exog) if exog is not None: if row_labels is None: row_labels = getattr(exog, 'index', None) if callable(row_labels): row_labels = None exog = np.asarray(exog) if exog.ndim == 1: # Params informs whether a row or column vector if self.params.shape[0] > 1: exog = exog[None, :] else: exog = exog[:, None] exog = np.atleast_2d(exog) # needed in count model shape[1] else: exog = self.model.exog if weights is None: weights = getattr(self.model, 'weights', None) if row_labels is None: row_labels = getattr(, 'row_labels', None) # need to handle other arrays, TODO: is delegating to model possible ? if weights is not None: weights = np.asarray(weights) if (weights.size > 1 and (weights.ndim != 1 or weights.shape[0] == exog.shape[1])): raise ValueError('weights has wrong shape') if pred_kwds is None: pred_kwds = {} predicted_mean = self.model.predict(self.params, exog, **pred_kwds) covb = self.cov_params() var_pred_mean = (exog *, exog.T).T).sum(1) var_resid = self.scale # self.mse_resid / weights # TODO: check that we have correct scale, Refactor scale #??? # special case for now: if self.cov_type == 'fixed scale': var_resid = self.cov_kwds['scale'] if weights is not None: var_resid /= weights dist = ['norm', 't'][self.use_t] return PredictionResults(predicted_mean, var_pred_mean, var_resid, df=self.df_resid, dist=dist, row_labels=row_labels)

Last update: Jun 14, 2024