statsmodels.discrete.conditional_models.ConditionalPoisson¶

class statsmodels.discrete.conditional_models.ConditionalPoisson(endog, exog, missing='none', **kwargs)[source]

Fit a conditional Poisson regression model to grouped data.

Every group is implicitly given an intercept, but the model is fit using a conditional likelihood in which the intercepts are not present. Thus, intercept estimates are not given, but the other parameter estimates can be interpreted as being adjusted for any group-level confounders.

Parameters
endogarray-like

The response variable

exogarray-like

The covariates

groupsarray-like

Codes defining the groups. This is a required keyword parameter.

Attributes
endog_names

Names of endogenous variables

exog_names

Names of exogenous variables

Methods

 fit([start_params, method, maxiter, …]) Fit method for likelihood based models fit_regularized([method, alpha, …]) Return a regularized fit to a linear regression model. 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 (possibly re-initialize) a Model instance. loglike(params) Log-likelihood of model. predict(params[, exog]) After a model has been fit predict returns the fitted values. score(params) Score vector of model.