statsmodels.discrete.discrete_model.DiscreteModel.cdf DiscreteModel.cdf(X) [source] The cumulative distribution function of the model.

statsmodels.discrete.discrete_model.Probit class statsmodels.discrete.discrete_model.Probit(endog, exog, **kwargs) [source] Binary choice Probit model Parameters: endog : array-like 1-d endogenous response variable. The dependent variable. exog : array-like A nobs x k array where nobs is the number of observations and k is the number of regressors. An intercept is not included by default and should be added by the user. See statsmodels.tools.add_constant. missing : str Available option

statsmodels.discrete.discrete_model.Poisson class statsmodels.discrete.discrete_model.Poisson(endog, exog, offset=None, exposure=None, missing='none', **kwargs) [source] Poisson model for count data Parameters: endog : array-like 1-d endogenous response variable. The dependent variable. exog : array-like A nobs x k array where nobs is the number of observations and k is the number of regressors. An intercept is not included by default and should be added by the user. See statsmodels.tool

statsmodels.discrete.discrete_model.NegativeBinomialResults class statsmodels.discrete.discrete_model.NegativeBinomialResults(model, mlefit, cov_type='nonrobust', cov_kwds=None, use_t=None) [source] A results class for NegativeBinomial 1 and 2 Parameters: model : A DiscreteModel instance params : array-like The parameters of a fitted model. hessian : array-like The hessian of the fitted model. scale : float A scale parameter for the covariance matrix. Returns: *Attributes* : aic : f

statsmodels.discrete.discrete_model.NegativeBinomial class statsmodels.discrete.discrete_model.NegativeBinomial(endog, exog, loglike_method='nb2', offset=None, exposure=None, missing='none', **kwargs) [source] Negative Binomial Model for count data Parameters: endog : array-like 1-d endogenous response variable. The dependent variable. exog : array-like A nobs x k array where nobs is the number of observations and k is the number of regressors. An intercept is not included by default and

statsmodels.discrete.discrete_model.MultinomialResults class statsmodels.discrete.discrete_model.MultinomialResults(model, mlefit, cov_type='nonrobust', cov_kwds=None, use_t=None) [source] A results class for multinomial data Parameters: model : A DiscreteModel instance params : array-like The parameters of a fitted model. hessian : array-like The hessian of the fitted model. scale : float A scale parameter for the covariance matrix. Returns: *Attributes* : aic : float Akaike infor

statsmodels.discrete.discrete_model.MultinomialModel class statsmodels.discrete.discrete_model.MultinomialModel(endog, exog, **kwargs) [source] Methods cdf(X) The cumulative distribution function of the model. cov_params_func_l1(likelihood_model, xopt, ...) Computes cov_params on a reduced parameter space corresponding to the nonzero parameters resulting from the l1 regularized fit. fit([start_params, method, maxiter, ...]) Fit the model using maximum likelihood. fit_regularized([start_p

statsmodels.discrete.discrete_model.MNLogit class statsmodels.discrete.discrete_model.MNLogit(endog, exog, **kwargs) [source] Multinomial logit model Parameters: endog : array-like endog is an 1-d vector of the endogenous response. endog can contain strings, ints, or floats. Note that if it contains strings, every distinct string will be a category. No stripping of whitespace is done. exog : array-like A nobs x k array where nobs is the number of observations and k is the number of regre

statsmodels.discrete.discrete_model.LogitResults class statsmodels.discrete.discrete_model.LogitResults(model, mlefit, cov_type='nonrobust', cov_kwds=None, use_t=None) [source] A results class for Logit Model Parameters: model : A DiscreteModel instance params : array-like The parameters of a fitted model. hessian : array-like The hessian of the fitted model. scale : float A scale parameter for the covariance matrix. Returns: *Attributes* : aic : float Akaike information criterion.

statsmodels.discrete.discrete_model.Logit class statsmodels.discrete.discrete_model.Logit(endog, exog, **kwargs) [source] Binary choice logit model Parameters: endog : array-like 1-d endogenous response variable. The dependent variable. exog : array-like A nobs x k array where nobs is the number of observations and k is the number of regressors. An intercept is not included by default and should be added by the user. See statsmodels.tools.add_constant. missing : str Available options a