3.6.11.1.13. statsmodels.graphics.regressionplots.wls_prediction_std¶

statsmodels.graphics.regressionplots.wls_prediction_std(res, exog=None, weights=None, alpha=0.05)[source]¶

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