# statsmodels.miscmodels.count.PoissonOffsetGMLE¶

class statsmodels.miscmodels.count.PoissonOffsetGMLE(endog, exog=None, offset=None, missing='none', **kwds)[source]

Maximum Likelihood Estimation of Poisson Model

This is an example for generic MLE which has the same statistical model as discretemod.Poisson but adds offset

Except for defining the negative log-likelihood method, all methods and results are generic. Gradients and Hessian and all resulting statistics are based on numerical differentiation.

Attributes
endog_names

Names of endogenous variables

exog_names

Names of exogenous variables

Methods

 expandparams(params) expand to full parameter array when some parameters are fixed fit([start_params, method, maxiter, …]) Fit the model using maximum likelihood. from_formula(formula, data[, subset, drop_cols]) Create a Model from a formula and dataframe. hessian(params) Hessian of log-likelihood evaluated at params hessian_factor(params[, scale, observed]) Weights for calculating Hessian information(params) Fisher information matrix of model Initialize (possibly re-initialize) a Model instance. loglike(params) Log-likelihood of model at params loglikeobs(params) Log-likelihood of individual observations at params nloglike(params) Negative log-likelihood of model at params nloglikeobs(params) Loglikelihood of Poisson model predict(params[, exog]) After a model has been fit predict returns the fitted values. reduceparams(params) Reduce parameters score(params) Gradient of log-likelihood evaluated at params score_obs(params, **kwds) Jacobian/Gradient of log-likelihood evaluated at params for each observation.