statsmodels.tsa.x13.x13_arima_select_order statsmodels.tsa.x13.x13_arima_select_order(endog, maxorder=(2, 1), maxdiff=(2, 1), diff=None, exog=None, log=None, outlier=True, trading=False, forecast_years=None, start=None, freq=None, print_stdout=False, x12path=None, prefer_x13=True) [source] Perform automatic seaonal ARIMA order identification using x12/x13 ARIMA. Parameters: endog : array-like, pandas.Series The series to model. It is best to use a pandas object with a DatetimeIndex or Peri

statsmodels.miscmodels.count.PoissonGMLE.fit PoissonGMLE.fit(start_params=None, method='nm', maxiter=500, full_output=1, disp=1, callback=None, retall=0, **kwargs) Fit the model using maximum likelihood. The rest of the docstring is from statsmodels.LikelihoodModel.fit

statsmodels.miscmodels.count.PoissonOffsetGMLE.nloglikeobs PoissonOffsetGMLE.nloglikeobs(params) [source] Loglikelihood of Poisson model Parameters: params : array-like The parameters of the model. Returns: The log likelihood of the model evaluated at `params` : Notes

statsmodels.miscmodels.count.PoissonOffsetGMLE.expandparams PoissonOffsetGMLE.expandparams(params) expand to full parameter array when some parameters are fixed Parameters: params : array reduced parameter array Returns: paramsfull : array expanded parameter array where fixed parameters are included Notes Calling this requires that self.fixed_params and self.fixed_paramsmask are defined. developer notes: This can be used in the log-likelihood to ... this could also be replaced by a m

statsmodels.sandbox.distributions.extras.NormExpan_gen.var NormExpan_gen.var(*args, **kwds) Variance 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: var : float the variance of the distribution

statsmodels.discrete.discrete_model.MNLogit.loglikeobs MNLogit.loglikeobs(params) [source] Log-likelihood of the multinomial logit model for each observation. Parameters: params : array-like The parameters of the multinomial logit model. Returns: loglike : ndarray (nobs,) The log likelihood for each observation of the model evaluated at params. See Notes Notes for observations where if individual i chose alternative j and 0 if not.

statsmodels.regression.quantile_regression.QuantRegResults.save QuantRegResults.save(fname, remove_data=False) save a pickle of this instance Parameters: fname : string or filehandle fname can be a string to a file path or filename, or a filehandle. remove_data : bool If False (default), then the instance is pickled without changes. If True, then all arrays with length nobs are set to None before pickling. See the remove_data method. In some cases not all arrays will be set to None. No

statsmodels.discrete.discrete_model.NegativeBinomial.from_formula classmethod NegativeBinomial.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

statsmodels.genmod.cov_struct.CovStruct.update CovStruct.update(params) [source] Updates the association parameter values based on the current regression coefficients. Parameters: params : array-like Working values for the regression parameters.

statsmodels.sandbox.tsa.fftarma.ArmaFft.acf ArmaFft.acf(nobs=None) theoretical autocorrelation function of an ARMA process Parameters: ar : array_like, 1d coefficient for autoregressive lag polynomial, including zero lag ma : array_like, 1d coefficient for moving-average lag polynomial, including zero lag nobs : int number of terms (lags plus zero lag) to include in returned acf Returns: acf : array autocorrelation of ARMA process given by ar, ma See also arma_acovf, acf, acovf