statsmodels.miscmodels.count.PoissonOffsetGMLE.nloglikeobs PoissonOffsetGMLE.nloglikeobs(params) [source] Loglikelihood of Poisson model Parameters: params : array-like The parameters of the model. Returns: The log likelihood of the model evaluated at `params` : Notes

statsmodels.miscmodels.count.PoissonOffsetGMLE.nloglike PoissonOffsetGMLE.nloglike(params)

statsmodels.miscmodels.count.PoissonOffsetGMLE.loglikeobs PoissonOffsetGMLE.loglikeobs(params)

statsmodels.miscmodels.count.PoissonOffsetGMLE.loglike PoissonOffsetGMLE.loglike(params)

statsmodels.miscmodels.count.PoissonOffsetGMLE.jac PoissonOffsetGMLE.jac(*args, **kwds) jac is deprecated, use score_obs instead! Use score_obs method. jac will be removed in 0.7. Jacobian/Gradient of log-likelihood evaluated at params for each observation.

statsmodels.miscmodels.count.PoissonOffsetGMLE.initialize PoissonOffsetGMLE.initialize()

statsmodels.miscmodels.count.PoissonOffsetGMLE.information PoissonOffsetGMLE.information(params) Fisher information matrix of model Returns -Hessian of loglike evaluated at params.

statsmodels.miscmodels.count.PoissonOffsetGMLE.hessian PoissonOffsetGMLE.hessian(params) Hessian of log-likelihood evaluated at params

statsmodels.miscmodels.count.PoissonOffsetGMLE.from_formula classmethod PoissonOffsetGMLE.from_formula(formula, data, subset=None, *args, **kwargs) Create a Model from a formula and dataframe. Parameters: formula : str or generic Formula object The formula specifying the model data : array-like The data for the model. See Notes. subset : array-like An array-like object of booleans, integers, or index values that indicate the subset of df to use in the model. Assumes df is a pandas.Data

statsmodels.miscmodels.count.PoissonOffsetGMLE.fit PoissonOffsetGMLE.fit(start_params=None, method='nm', maxiter=500, full_output=1, disp=1, callback=None, retall=0, **kwargs) Fit the model using maximum likelihood. The rest of the docstring is from statsmodels.LikelihoodModel.fit