statsmodels.discrete.discrete_model.Poisson.from_formula classmethod Poisson.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 :

statsmodels.discrete.discrete_model.BinaryResults.aic static BinaryResults.aic()

statsmodels.tools.eval_measures.aic_sigma statsmodels.tools.eval_measures.aic_sigma(sigma2, nobs, df_modelwc, islog=False) [source] Akaike information criterion 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 constant Returns: aic : float inform

statsmodels.discrete.discrete_model.BinaryResults.load classmethod BinaryResults.load(fname) load a pickle, (class method) Parameters: fname : string or filehandle fname can be a string to a file path or filename, or a filehandle. Returns: unpickled instance :

statsmodels.sandbox.tsa.fftarma.ArmaFft.from_estimation classmethod ArmaFft.from_estimation(model_results, nobs=None) Create ArmaProcess instance from ARMA estimation results Parameters: model_results : ARMAResults instance A fitted model nobs : int, optional If None, nobs is taken from the results

statsmodels.sandbox.distributions.transformed.TransfTwo_gen.sf TransfTwo_gen.sf(x, *args, **kwds) Survival function (1-cdf) 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: sf : array_like Survival function evalu

statsmodels.iolib.table.SimpleTable class statsmodels.iolib.table.SimpleTable(data, headers=None, stubs=None, title='', datatypes=None, csv_fmt=None, txt_fmt=None, ltx_fmt=None, html_fmt=None, celltype=None, rowtype=None, **fmt_dict) [source] Produce a simple ASCII, CSV, HTML, or LaTeX table from a rectangular (2d!) array of data, not necessarily numerical. Directly supports at most one header row, which should be the length of data[0]. Directly supports at most one stubs column, which must

statsmodels.tsa.vector_ar.var_model.VARResults.sigma_u_mle static VARResults.sigma_u_mle() [source] (Biased) maximum likelihood estimate of noise process covariance

statsmodels.tsa.arima_process.ArmaProcess.periodogram ArmaProcess.periodogram(nobs=None) [source] periodogram for ARMA process given by lag-polynomials ar and ma Parameters: ar : array_like autoregressive lag-polynomial with leading 1 and lhs sign ma : array_like moving average lag-polynomial with leading 1 worN : {None, int}, optional option for scipy.signal.freqz (read ?w or N?) If None, then compute at 512 frequencies around the unit circle. If a single integer, the compute at that

statsmodels.regression.linear_model.OLSResults.cov_HC3 static OLSResults.cov_HC3() See statsmodels.RegressionResults