statsmodels.tsa.vector_ar.dynamic.DynamicVAR.equations static DynamicVAR.equations() [source]

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

statsmodels.sandbox.distributions.transformed.Transf_gen.pdf Transf_gen.pdf(x, *args, **kwds) Probability density function at x of the given RV. Parameters: x : array_like quantiles 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: pdf : ndarray Probability density functi

statsmodels.regression.mixed_linear_model.MixedLMResults.bse_re static MixedLMResults.bse_re() [source] Returns the standard errors of the variance parameters. Note that the sampling distribution of variance parameters is strongly skewed unless the sample size is large, so these standard errors may not give meaningful confidence intervals of p-values if used in the usual way.

statsmodels.graphics.tsaplots.quarter_plot statsmodels.graphics.tsaplots.quarter_plot(x, dates=None, ylabel=None, ax=None) [source] Seasonal plot of quarterly data Parameters: x : array-like Seasonal data to plot. If dates is None, x must be a pandas object with a PeriodIndex or DatetimeIndex with a monthly frequency. dates : array-like, optional If x is not a pandas object, then dates must be supplied. ylabel : str, optional The label for the y-axis. Will attempt to use the name attri

statsmodels.graphics.gofplots.ProbPlot.qqplot ProbPlot.qqplot(xlabel=None, ylabel=None, line=None, other=None, ax=None, **plotkwargs) [source] Q-Q plot of the quantiles of x versus the quantiles/ppf of a distribution or the quantiles of another ProbPlot instance. Parameters: xlabel, ylabel : str or None, optional User-provided lables for the x-axis and y-axis. If None (default), other values are used depending on the status of the kwarg other. line : str {?45?, ?s?, ?r?, q?} or None, opti

statsmodels.stats.inter_rater.fleiss_kappa statsmodels.stats.inter_rater.fleiss_kappa(table) [source] Fleiss? kappa multi-rater agreement measure Parameters: table : array_like, 2-D assumes subjects in rows, and categories in columns Returns: kappa : float Fleiss?s kappa statistic for inter rater agreement Notes coded from Wikipedia page http://en.wikipedia.org/wiki/Fleiss%27_kappa no variance or tests yet

statsmodels.regression.linear_model.WLS.from_formula classmethod WLS.from_formula(formula, data, subset=None, *args, **kwargs) Create a Model from a formula and dataframe. Parameters: formula : str or generic Formula object The formula specifying the model data : array-like The data for the model. See Notes. subset : array-like An array-like object of booleans, integers, or index values that indicate the subset of df to use in the model. Assumes df is a pandas.DataFrame args : extra a

statsmodels.genmod.families.family.Family.resid_dev Family.resid_dev(endog, mu, scale=1.0) [source] The deviance residuals Parameters: endog : array The endogenous response variable mu : array The inverse of the link function at the linear predicted values. scale : float, optional An optional argument to divide the residuals by scale Returns: Deviance residuals. : Notes The deviance residuals are defined for each family.

statsmodels.tsa.vector_ar.var_model.VARResults.is_stable VARResults.is_stable(verbose=False) Determine stability based on model coefficients Parameters: verbose : bool Print eigenvalues of the VAR(1) companion Notes Checks if det(I - Az) = 0 for any mod(z) <= 1, so all the eigenvalues of the companion matrix must lie outside the unit circle