some new features

.venv/lib/python3.12/site-packages/statsmodels/sandbox/regression/predstd.py

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+'''Additional functions
+
+prediction standard errors and confidence intervals
+
+
+A: josef pktd
+'''
+
+import numpy as np
+from scipy import stats
+
+def atleast_2dcol(x):
+    ''' convert array_like to 2d from 1d or 0d
+
+    not tested because not used
+    '''
+    x = np.asarray(x)
+    if (x.ndim == 1):
+        x = x[:, None]
+    elif (x.ndim == 0):
+        x = np.atleast_2d(x)
+    elif (x.ndim > 0):
+        raise ValueError('too many dimensions')
+    return x
+
+
+def wls_prediction_std(res, exog=None, weights=None, alpha=0.05):
+    '''calculate standard deviation and confidence interval for prediction
+
+    applies to WLS and OLS, not to general GLS,
+    that is independently but not identically distributed observations
+
+    Parameters
+    ----------
+    res : regression result instance
+        results of WLS or OLS regression required attributes see notes
+    exog : array_like (optional)
+        exogenous variables for points to predict
+    weights : scalar or array_like (optional)
+        weights as defined for WLS (inverse of variance of observation)
+    alpha : float (default: alpha = 0.05)
+        confidence level for two-sided hypothesis
+
+    Returns
+    -------
+    predstd : array_like, 1d
+        standard error of prediction
+        same length as rows of exog
+    interval_l, interval_u : array_like
+        lower und upper confidence bounds
+
+    Notes
+    -----
+    The result instance needs to have at least the following
+    res.model.predict() : predicted values or
+    res.fittedvalues : values used in estimation
+    res.cov_params() : covariance matrix of parameter estimates
+
+    If exog is 1d, then it is interpreted as one observation,
+    i.e. a row vector.
+
+    testing status: not compared with other packages
+
+    References
+    ----------
+
+    Greene p.111 for OLS, extended to WLS by analogy
+
+    '''
+    # work around current bug:
+    #    fit does not attach results to model, predict broken
+    #res.model.results
+
+    covb = res.cov_params()
+    if exog is None:
+        exog = res.model.exog
+        predicted = res.fittedvalues
+        if weights is None:
+            weights = res.model.weights
+    else:
+        exog = np.atleast_2d(exog)
+        if covb.shape[1] != exog.shape[1]:
+            raise ValueError('wrong shape of exog')
+        predicted = res.model.predict(res.params, exog)
+        if weights is None:
+            weights = 1.
+        else:
+            weights = np.asarray(weights)
+            if weights.size > 1 and len(weights) != exog.shape[0]:
+                raise ValueError('weights and exog do not have matching shape')
+
+
+    # full covariance:
+    #predvar = res3.mse_resid + np.diag(np.dot(X2,np.dot(covb,X2.T)))
+    # predication variance only
+    predvar = res.mse_resid/weights + (exog * np.dot(covb, exog.T).T).sum(1)
+    predstd = np.sqrt(predvar)
+    tppf = stats.t.isf(alpha/2., res.df_resid)
+    interval_u = predicted + tppf * predstd
+    interval_l = predicted - tppf * predstd
+    return predstd, interval_l, interval_u