statsmodels.regression.linear_model.RegressionResults.mse_model static RegressionResults.mse_model() [source]

statsmodels.stats.diagnostic.compare_j statsmodels.stats.diagnostic.compare_j = J-Test for comparing non-nested models Parameters: results_x : Result instance result instance of first model results_z : Result instance result instance of second model attach : bool From description in Greene, section 8.3.3 : produces correct results for Example 8.3, Greene - not checked yet : #currently an exception, but I don?t have clean reload in python session : check what results should be attached

statsmodels.sandbox.distributions.transformed.squarenormalg statsmodels.sandbox.distributions.transformed.squarenormalg = Distribution based on a non-monotonic (u- or hump-shaped transformation) the constructor can be called with a distribution class, and functions that define the non-linear transformation. and generates the distribution of the transformed random variable Note: the transformation, it?s inverse and derivatives need to be fully specified: func, funcinvplus, funcinvminus, deri

statsmodels.regression.quantile_regression.QuantRegResults.cov_HC0 static QuantRegResults.cov_HC0() See statsmodels.RegressionResults

Interactions and ANOVA Link to Notebook GitHub Note: This script is based heavily on Jonathan Taylor's class notes http://www.stanford.edu/class/stats191/interactions.html Download and format data: In [1]: from __future__ import print_function from statsmodels.compat import urlopen import numpy as np np.set_printoptions(precision=4, suppress=True) import statsmodels.api as sm import pandas as pd pd.set_option("display.width", 100) import matplotlib.pyplot as plt from statsmodels.fo

statsmodels.duration.hazard_regression.PHRegResults.schoenfeld_residuals static PHRegResults.schoenfeld_residuals() [source] A matrix containing the Schoenfeld residuals. Notes Schoenfeld residuals for censored observations are set to zero.

Pitfalls This page lists issues which may arise while using statsmodels. These can be the result of data-related or statistical problems, software design, ?non-standard? use of models, or edge cases. statsmodels provides several warnings and helper functions for diagnostic checking (see this blog article for an example of misspecification checks in linear regression). The coverage is of course not comprehensive, but more warnings and diagnostic functions will be added over time. While the under

statsmodels.duration.hazard_regression.PHRegResults.martingale_residuals static PHRegResults.martingale_residuals() [source] The martingale residuals.

statsmodels.discrete.discrete_model.CountResults.tvalues static CountResults.tvalues() Return the t-statistic for a given parameter estimate.

statsmodels.discrete.discrete_model.BinaryResults.llf static BinaryResults.llf()