statsmodels.genmod.families.family.NegativeBinomial.deviance NegativeBinomial.deviance(endog, mu, scale=1.0) [source] Returns the value of the deviance function. Parameters: endog : array-like Endogenous response variable mu : array-like Fitted mean response variable scale : float, optional An optional scale argument Returns: deviance : float Deviance function as defined below Notes deviance = sum(piecewise) where piecewise is defined as If : If :

statsmodels.genmod.families.links.NegativeBinomial.deriv NegativeBinomial.deriv(p) [source] Derivative of the negative binomial transform Parameters: p : array-like Mean parameters Returns: g?(p) : array The derivative of the negative binomial transform link function Notes g?(x) = 1/(x+alpha*x^2)

statsmodels.discrete.discrete_model.NegativeBinomial.cov_params_func_l1 NegativeBinomial.cov_params_func_l1(likelihood_model, xopt, retvals) Computes cov_params on a reduced parameter space corresponding to the nonzero parameters resulting from the l1 regularized fit. Returns a full cov_params matrix, with entries corresponding to zero?d values set to np.nan.

statsmodels.discrete.discrete_model.NegativeBinomial.cdf NegativeBinomial.cdf(X) The cumulative distribution function of the model.

statsmodels.genmod.families.links.nbinom.inverse_deriv nbinom.inverse_deriv(z) Derivative of the inverse of the negative binomial transform Parameters: z : array-like Usually the linear predictor for a GLM or GEE model Returns: The value of the inverse of the derivative of the negative binomial : link :

statsmodels.genmod.families.links.nbinom.inverse nbinom.inverse(z) Inverse of the negative binomial transform Parameters: z : array-like The value of the inverse of the negative binomial link at p. Returns : ??- : p : array Mean parameters Notes g^(-1)(z) = exp(z)/(alpha*(1-exp(z)))

statsmodels.discrete.discrete_model.MultinomialResults.wald_test MultinomialResults.wald_test(r_matrix, cov_p=None, scale=1.0, invcov=None, use_f=None) Compute a Wald-test for a joint linear hypothesis. Parameters: r_matrix : array-like, str, or tuple array : An r x k array where r is the number of restrictions to test and k is the number of regressors. It is assumed that the linear combination is equal to zero. str : The full hypotheses to test can be given as a string. See the examples.

statsmodels.genmod.families.links.nbinom.deriv nbinom.deriv(p) Derivative of the negative binomial transform Parameters: p : array-like Mean parameters Returns: g?(p) : array The derivative of the negative binomial transform link function Notes g?(x) = 1/(x+alpha*x^2)

statsmodels.discrete.discrete_model.MultinomialResults.summary2 MultinomialResults.summary2(alpha=0.05, float_format='%.4f') [source] Experimental function to summarize regression results Parameters: alpha : float significance level for the confidence intervals float_format: string : print format for floats in parameters summary Returns: smry : Summary instance this holds the summary tables and text, which can be printed or converted to various output formats. See also statsmode

statsmodels.discrete.discrete_model.MultinomialResults.t_test MultinomialResults.t_test(r_matrix, cov_p=None, scale=None, use_t=None) Compute a t-test for a each linear hypothesis of the form Rb = q Parameters: r_matrix : array-like, str, tuple array : If an array is given, a p x k 2d array or length k 1d array specifying the linear restrictions. It is assumed that the linear combination is equal to zero. str : The full hypotheses to test can be given as a string. See the examples. tuple :