statsmodels.genmod.generalized_linear_model.GLMResults.resid_deviance static GLMResults.resid_deviance() [source]

statsmodels.tsa.arima_model.ARIMA.predict ARIMA.predict(params, start=None, end=None, exog=None, typ='linear', dynamic=False) [source] ARIMA model in-sample and out-of-sample prediction Parameters: params : array-like The fitted parameters of the model. start : int, str, or datetime Zero-indexed observation number at which to start forecasting, ie., the first forecast is start. Can also be a date string to parse or a datetime type. end : int, str, or datetime Zero-indexed observation n

statsmodels.robust.robust_linear_model.RLMResults.sresid static RLMResults.sresid() [source]

statsmodels.regression.mixed_linear_model.MixedLMResults.random_effects static MixedLMResults.random_effects() [source] Returns the conditional means of all random effects given the data. Returns: random_effects : DataFrame A DataFrame with the distinct group values as the index and the conditional means of the random effects in the columns.

statsmodels.stats.weightstats.DescrStatsW.ztost_mean DescrStatsW.ztost_mean(low, upp) [source] test of (non-)equivalence of one sample, based on z-test TOST: two one-sided z-tests null hypothesis: m < low or m > upp alternative hypothesis: low < m < upp where m is the expected value of the sample (mean of the population). If the pvalue is smaller than a threshold, say 0.05, then we reject the hypothesis that the expected value of the sample (mean of the population) is outside of

statsmodels.sandbox.distributions.transformed.TransfTwo_gen.rvs TransfTwo_gen.rvs(*args, **kwds) Random variates of given type. Parameters: arg1, arg2, arg3,... : array_like The shape parameter(s) for the distribution (see docstring of the instance object for more information). loc : array_like, optional Location parameter (default=0). scale : array_like, optional Scale parameter (default=1). size : int or tuple of ints, optional Defining number of random variates (default=1). Retu

statsmodels.genmod.generalized_estimating_equations.GEEResults.resid static GEEResults.resid() [source] Returns the residuals, the endogeneous data minus the fitted values from the model.

statsmodels.genmod.families.family.Gaussian.predict Gaussian.predict(mu) Linear predictors based on given mu values. Parameters: mu : array The mean response variables Returns: lin_pred : array Linear predictors based on the mean response variables. The value of the link function at the given mu.

statsmodels.stats.weightstats.DescrStatsW.get_compare DescrStatsW.get_compare(other, weights=None) [source] return an instance of CompareMeans with self and other Parameters: other : array_like or instance of DescrStatsW If array_like then this creates an instance of DescrStatsW with the given weights. weights : None or array weights are only used if other is not an instance of DescrStatsW Returns: cm : instance of CompareMeans the instance has self attached as d1 and other as d2.

statsmodels.graphics.regressionplots.influence_plot statsmodels.graphics.regressionplots.influence_plot(results, external=True, alpha=0.05, criterion='cooks', size=48, plot_alpha=0.75, ax=None, **kwargs) [source] Plot of influence in regression. Plots studentized resids vs. leverage. Parameters: results : results instance A fitted model. external : bool Whether to use externally or internally studentized residuals. It is recommended to leave external as True. alpha : float The alpha va