statsmodels.discrete.discrete_model.CountResults.pvalues static CountResults.pvalues()

statsmodels.graphics.correlation.plot_corr_grid statsmodels.graphics.correlation.plot_corr_grid(dcorrs, titles=None, ncols=None, normcolor=False, xnames=None, ynames=None, fig=None, cmap='RdYlBu_r') [source] Create a grid of correlation plots. The individual correlation plots are assumed to all have the same variables, axis labels can be specified only once. Parameters: dcorrs : list or iterable of ndarrays List of correlation matrices. titles : list of str, optional List of titles for t

statsmodels.sandbox.distributions.transformed.ExpTransf_gen.stats ExpTransf_gen.stats(*args, **kwds) Some statistics of the given RV 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 (discrete RVs only) scale parameter (default=1) moments : str, optional composed of letters [?mvsk?] defining which mo

statsmodels.sandbox.distributions.extras.pdf_moments statsmodels.sandbox.distributions.extras.pdf_moments(cnt) [source] Return the Gaussian expanded pdf function given the list of central moments (first one is mean). Changed so it works only if four arguments are given. Uses explicit formula, not loop. Notes This implements a Gram-Charlier expansion of the normal distribution where the first 2 moments coincide with those of the normal distribution but skew and kurtosis can deviate from it. I

statsmodels.sandbox.sysreg.SUR.predict SUR.predict(design) [source]

statsmodels.discrete.discrete_model.ProbitResults.bic static ProbitResults.bic()

statsmodels.discrete.discrete_model.CountResults.summary CountResults.summary(yname=None, xname=None, title=None, alpha=0.05, yname_list=None) Summarize the Regression Results Parameters: yname : string, optional Default is y xname : list of strings, optional Default is var_## for ## in p the number of regressors title : string, optional Title for the top table. If not None, then this replaces the default title alpha : float significance level for the confidence intervals Returns:

statsmodels.iolib.table.SimpleTable.as_text SimpleTable.as_text(**fmt_dict) [source] Return string, the table as text.

statsmodels.tools.tools.recipr statsmodels.tools.tools.recipr(X) [source] Return the reciprocal of an array, setting all entries less than or equal to 0 to 0. Therefore, it presumes that X should be positive in general.

statsmodels.miscmodels.count.PoissonOffsetGMLE.score_obs PoissonOffsetGMLE.score_obs(params, **kwds) Jacobian/Gradient of log-likelihood evaluated at params for each observation.