statsmodels.regression.linear_model.OLSResults.fittedvalues static OLSResults.fittedvalues()

statsmodels.tsa.arima_model.ARMA.initialize ARMA.initialize() Initialize (possibly re-initialize) a Model instance. For instance, the design matrix of a linear model may change and some things must be recomputed.

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

statsmodels.tsa.vector_ar.irf.IRAnalysis.fevd_table IRAnalysis.fevd_table() [source]

statsmodels.discrete.discrete_model.BinaryResults.wald_test BinaryResults.wald_test(r_matrix, cov_p=None, scale=1.0, invcov=None, use_f=None) Compute a Wald-test for a joint linear hypothesis. Parameters: r_matrix : array-like, str, or tuple array : An r x k array where r is the number of restrictions to test and k is the number of regressors. It is assumed that the linear combination is equal to zero. str : The full hypotheses to test can be given as a string. See the examples. tuple : A

statsmodels.stats.weightstats.DescrStatsW.var static DescrStatsW.var() [source] variance with default degrees of freedom correction

statsmodels.tsa.arima_model.ARIMAResults.pvalues static ARIMAResults.pvalues()

Sandbox This sandbox contains code that is for various resons not ready to be included in statsmodels proper. It contains modules from the old stats.models code that have not been tested, verified and updated to the new statsmodels structure: cox survival model, mixed effects model with repeated measures, generalized additive model and the formula framework. The sandbox also contains code that is currently being worked on until it fits the pattern of statsmodels or is sufficiently tested. All s

statsmodels.stats.diagnostic.unitroot_adf statsmodels.stats.diagnostic.unitroot_adf(x, maxlag=None, trendorder=0, autolag='AIC', store=False)

statsmodels.discrete.discrete_model.NegativeBinomialResults.prsquared static NegativeBinomialResults.prsquared()