statsmodels.discrete.discrete_model.CountModel

class statsmodels.discrete.discrete_model.CountModel(endog, exog, offset=None, exposure=None, missing='none', **kwargs)

Attributes

endog_names	Names of endogenous variables
exog_names	Names of exogenous variables

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)	The Hessian matrix of the model
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)	Log-likelihood of model.
pdf(X)	The probability density (mass) function of the model.
predict(params[, exog, exposure, offset, linear])	Predict response variable of a count model given exogenous variables.
score(params)	Score vector of model.

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)	The Hessian matrix of the model
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)	Log-likelihood of model.
pdf(X)	The probability density (mass) function of the model.
predict(params[, exog, exposure, offset, linear])	Predict response variable of a count model given exogenous variables.
score(params)	Score vector of model.

Attributes

endog_names	Names of endogenous variables
exog_names	Names of exogenous variables