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.

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()

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.

Properties

endog_names

Names of endogenous variables.

exog_names

Names of exogenous variables.