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.sandbox.tsa.fftarma.ArmaFft.impulse_response ArmaFft.impulse_response(nobs=None) get the impulse response function (MA representation) for ARMA process Parameters: ma : array_like, 1d moving average lag polynomial ar : array_like, 1d auto regressive lag polynomial nobs : int number of observations to calculate Returns: ir : array, 1d impulse response function with nobs elements Notes This is the same as finding the MA representation of an ARMA(p,q). By reversing the r

statsmodels.discrete.discrete_model.MNLogit.jac MNLogit.jac(*args, **kwds) jac is deprecated, use score_obs instead! Use score_obs method. jac will be removed in 0.7 Jacobian matrix for multinomial logit model log-likelihood Parameters: params : array The parameters of the multinomial logit model. Returns: jac : ndarray, (nobs, k_vars*(J-1)) The derivative of the loglikelihood for each observation evaluated at params . Notes for , for observations In the multinomial model the sc

statsmodels.sandbox.distributions.transformed.LogTransf_gen.isf LogTransf_gen.isf(q, *args, **kwds) Inverse survival function at q of the given RV. Parameters: q : array_like upper tail probability 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: x : ndarray or scalar Qu

statsmodels.sandbox.distributions.transformed.LogTransf_gen.stats LogTransf_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.regression.linear_model.WLS.initialize WLS.initialize()

statsmodels.sandbox.regression.gmm.IVRegressionResults.summary IVRegressionResults.summary(yname=None, xname=None, title=None, alpha=0.05) [source] 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 Retu

statsmodels.miscmodels.count.PoissonOffsetGMLE class statsmodels.miscmodels.count.PoissonOffsetGMLE(endog, exog=None, offset=None, missing='none', **kwds) [source] Maximum Likelihood Estimation of Poisson Model This is an example for generic MLE which has the same statistical model as discretemod.Poisson but adds offset Except for defining the negative log-likelihood method, all methods and results are generic. Gradients and Hessian and all resulting statistics are based on numerical differe

statsmodels.emplike.descriptive.DescStatUV.test_skew DescStatUV.test_skew(skew0, return_weights=False) [source] Returns -2 x log-likelihood and p-value for the hypothesized skewness. Parameters: skew0 : float Skewness value to be tested return_weights : bool If True, function also returns the weights that maximize the likelihood ratio. Default is False. Returns: test_results : tuple The log-likelihood ratio and p_value of skew0

statsmodels.emplike.descriptive.DescStatUV.test_kurt DescStatUV.test_kurt(kurt0, return_weights=False) [source] Returns -2 x log-likelihood and the p-value for the hypothesized kurtosis. Parameters: kurt0 : float Kurtosis value to be tested return_weights : bool If True, function also returns the weights that maximize the likelihood ratio. Default is False. Returns: test_results : tuple The log-likelihood ratio and p-value of kurt0