statsmodels.discrete.discrete_model.CountModel

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