statsmodels.duration.hazard_regression.PHReg.fit PHReg.fit(groups=None, **args) [source] Fit a proportional hazards regression model. Parameters: groups : array-like Labels indicating groups of observations that may be dependent. If present, the standard errors account for this dependence. Does not affect fitted values. Returns a PHregResults instance. :

statsmodels.iolib.summary2.Summary.add_df Summary.add_df(df, index=True, header=True, float_format='%.4f', align='r') [source] Add the contents of a DataFrame to summary table Parameters: df : DataFrame header: bool : Reproduce the DataFrame column labels in summary table index: bool : Reproduce the DataFrame row labels in summary table float_format: string : Formatting to float data columns align : string Data alignment (l/c/r)

statsmodels.tsa.vector_ar.var_model.VARResults.mse VARResults.mse(steps) Compute theoretical forecast error variance matrices Parameters: steps : int Number of steps ahead Returns: forc_covs : ndarray (steps x neqs x neqs) Notes

statsmodels.genmod.cov_struct.Autoregressive.summary Autoregressive.summary() [source]

statsmodels.tools.numdiff.approx_hess_cs statsmodels.tools.numdiff.approx_hess_cs(x, f, epsilon=None, args=(), kwargs={}) [source] Calculate Hessian with complex-step derivative approximation Calculate Hessian with finite difference derivative approximation Parameters: x : array_like value at which function derivative is evaluated f : function function of one array f(x, *args, **kwargs) epsilon : float or array-like, optional Stepsize used, if None, then stepsize is automatically chose

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

statsmodels.duration.hazard_regression.PHReg.baseline_cumulative_hazard PHReg.baseline_cumulative_hazard(params) [source] Estimate the baseline cumulative hazard and survival functions. Parameters: params : ndarray The model parameters. Returns: A list of triples (time, hazard, survival) containing the time : values and corresponding cumulative hazard and survival : function values for each stratum. : Notes Uses the Nelson-Aalen estimator.

statsmodels.stats.power.FTestAnovaPower.power FTestAnovaPower.power(effect_size, nobs, alpha, k_groups=2) [source] Calculate the power of a F-test for one factor ANOVA. Parameters: effect_size : float standardized effect size, mean divided by the standard deviation. effect size has to be positive. nobs : int or float sample size, number of observations. alpha : float in interval (0,1) significance level, e.g. 0.05, is the probability of a type I error, that is wrong rejections if the N

statsmodels.stats.power.GofChisquarePower.power GofChisquarePower.power(effect_size, nobs, alpha, n_bins, ddof=0) [source] Calculate the power of a chisquare test for one sample Only two-sided alternative is implemented Parameters: effect_size : float standardized effect size, according to Cohen?s definition. see statsmodels.stats.gof.chisquare_effectsize nobs : int or float sample size, number of observations. alpha : float in interval (0,1) significance level, e.g. 0.05, is the proba

statsmodels.tsa.vector_ar.irf.IRAnalysis.plot_cum_effects IRAnalysis.plot_cum_effects(orth=False, impulse=None, response=None, signif=0.05, plot_params=None, subplot_params=None, plot_stderr=True, stderr_type='asym', repl=1000, seed=None) Plot cumulative impulse response functions Parameters: orth : bool, default False Compute orthogonalized impulse responses impulse : string or int variable providing the impulse response : string or int variable affected by the impulse signif : float