statsmodels.discrete.truncated_model.TruncatedLFNegativeBinomialP¶

class statsmodels.discrete.truncated_model.TruncatedLFNegativeBinomialP(endog, exog, offset=None, exposure=None, truncation=0, p=2, missing='none', **kwargs)[source]¶

Truncated Generalized Negative Binomial model for count data

Added in version 0.14.0.

Parameters:¶

endogarray_like

A 1-d endogenous response variable. The dependent variable.

exogarray_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.

offsetarray_like

Offset is added to the linear prediction with coefficient equal to 1.

exposurearray_like

Log(exposure) is added to the linear prediction with coefficient equal to 1.

missingstr

Available options are ‘none’, ‘drop’, and ‘raise’. If ‘none’, no nan checking is done. If ‘drop’, any observations with nans are dropped. If ‘raise’, an error is raised. Default is ‘none’.

Attributes:¶

endogarray

A reference to the endogenous response variable

exogarray

A reference to the exogenous design.

truncationint, optional

Truncation parameter specify truncation point out of the support of the distribution. pmf(k) = 0 for k <= truncation

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_params, method, ...])	Fit the model using a regularized maximum likelihood.
from_formula(formula, data[, subset, drop_cols])	Create a Model from a formula and dataframe.
hessian(params)	Generic Truncated model Hessian matrix of the loglikelihood
information(params)	Fisher information matrix of model.
initialize()	Initialize is called by statsmodels.model.LikelihoodModel.__init__ and should contain any preprocessing that needs to be done for a model.
loglike(params)	Loglikelihood of Generic Truncated model
loglikeobs(params)	Loglikelihood for observations of Generic Truncated model
pdf(X)	The probability density (mass) function of the model.
predict(params[, exog, exposure, offset, ...])	Predict response variable or other statistic given exogenous variables.
score(params)	Generic Truncated model score (gradient) vector of the log-likelihood
score_obs(params)	Generic Truncated model score (gradient) vector of the log-likelihood

Properties

endog_names	Names of endogenous variables.
exog_names	Names of exogenous variables.