statsmodels.stats.power.FTestPower.solve_power FTestPower.solve_power(effect_size=None, df_num=None, df_denom=None, nobs=None, alpha=None, power=None, ncc=1) [source] solve for any one parameter of the power of a F-test for the one sample F-test the keywords are: effect_size, df_num, df_denom, alpha, power Exactly one needs to be None, all others need numeric values. Parameters: effect_size : float standardized effect size, mean divided by the standard deviation. effect size has to be posi

statsmodels.stats.power.TTestPower.plot_power TTestPower.plot_power(dep_var='nobs', nobs=None, effect_size=None, alpha=0.05, ax=None, title=None, plt_kwds=None, **kwds) plot power with number of observations or effect size on x-axis Parameters: dep_var : string in [?nobs?, ?effect_size?, ?alpha?] This specifies which variable is used for the horizontal axis. If dep_var=?nobs? (default), then one curve is created for each value of effect_size. If dep_var=?effect_size? or alpha, then one cur

statsmodels.sandbox.regression.anova_nistcertified.anova_oneway statsmodels.sandbox.regression.anova_nistcertified.anova_oneway(y, x, seq=0) [source]

statsmodels.graphics.regressionplots.plot_leverage_resid2 statsmodels.graphics.regressionplots.plot_leverage_resid2(results, alpha=0.05, label_kwargs={}, ax=None, **kwargs) [source] Plots leverage statistics vs. normalized residuals squared Parameters: results : results instance A regression results instance alpha : float Specifies the cut-off for large-standardized residuals. Residuals are assumed to be distributed N(0, 1) with alpha=alpha. label_kwargs : dict The keywords to pass to

statsmodels.tools.numdiff.approx_hess3 statsmodels.tools.numdiff.approx_hess3(x, f, epsilon=None, args=(), kwargs={}) [source] 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 chosen according to EPS**(1/4)*x. args : tuple Arguments for functio

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

statsmodels.tools.eval_measures.aicc_sigma statsmodels.tools.eval_measures.aicc_sigma(sigma2, nobs, df_modelwc, islog=False) [source] Akaike information criterion (AIC) with small sample correction Parameters: sigma2 : float estimate of the residual variance or determinant of Sigma_hat in the multivariate case. If islog is true, then it is assumed that sigma is already log-ed, for example logdetSigma. nobs : int number of observations df_modelwc : int number of parameters including con

statsmodels.sandbox.distributions.transformed.Transf_gen.mean Transf_gen.mean(*args, **kwds) Mean of the distribution 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) Returns: mean : float the mean of the distribution

statsmodels.sandbox.distributions.transformed.ExpTransf_gen.median ExpTransf_gen.median(*args, **kwds) Median of the distribution. 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 is 0. scale : array_like, optional Scale parameter, Default is 1. Returns: median : float The median of the distribution. See also stats.distribut

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