statsmodels.discrete.discrete_model.CountModel

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

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