static IVRegressionResults.fittedvalues()

statsmodels.sandbox.regression.gmm.IVRegressionResults.fittedvalues static IVRegressionResults.fittedvalues()

OLS.initialize()

statsmodels.regression.linear_model.OLS.initialize OLS.initialize()

static VARResults.fittedvalues()

statsmodels.tsa.vector_ar.var_model.VARResults.fittedvalues static VARResults.fittedvalues() [source] The predicted insample values of the response variables of the model.

IV2SLS.information()

statsmodels.sandbox.regression.gmm.IV2SLS.information IV2SLS.information(params) Fisher information matrix of model Returns -Hessian of loglike evaluated at params.

sandbox.regression.try_catdata.groupsstats_dummy()

statsmodels.sandbox.regression.try_catdata.groupsstats_dummy statsmodels.sandbox.regression.try_catdata.groupsstats_dummy(y, x, nonseq=0) [source]

static RegressionResults.condition_number()

statsmodels.regression.linear_model.RegressionResults.condition_number static RegressionResults.condition_number() [source] Return condition number of exogenous matrix. Calculated as ratio of largest to smallest eigenvalue.

static RegressionResults.mse_model()

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

static QuantRegResults.cov_HC0()

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

Interactions and ANOVA

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

Pitfalls

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