statsmodels.discrete.conditional_models.ConditionalPoisson

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

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.

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.

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()	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

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()	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

Properties

endog_names	Names of endogenous variables
exog_names	Names of exogenous variables