statsmodels.miscmodels.count.PoissonZiGMLE

class statsmodels.miscmodels.count.PoissonZiGMLE(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 and zero-inflation.

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.

There are numerical problems if there is no zero-inflation.

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.
loglikeobs(params)
nloglike(params)
nloglikeobs(params) Loglikelihood of Poisson model
predict(params[, exog]) After a model has been fit predict returns the fitted values.
reduceparams(params)
score(params) Gradient of log-likelihood evaluated at params
score_obs(params, **kwds) Jacobian/Gradient of log-likelihood evaluated at params for each observation.

Attributes

endog_names Names of endogenous variables
exog_names Names of exogenous variables

© 2009–2012 Statsmodels Developers
© 2006–2008 Scipy Developers
© 2006 Jonathan E. Taylor
Licensed under the 3-clause BSD License.
http://www.statsmodels.org/stable/generated/statsmodels.miscmodels.count.PoissonZiGMLE.html