3.7.1.1.3. statsmodels.miscmodels.api.PoissonZiGMLE¶
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class
statsmodels.miscmodels.api.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.
3.7.1.1.3.1. Methods¶
__init__(endog[, exog, offset, missing]) |
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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]) |
Create a Model from a formula and dataframe. |
hessian(params) |
Hessian of log-likelihood evaluated at params |
information(params) |
Fisher information matrix of model |
initialize() |
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jac(*args, **kwds) |
jac is deprecated, use score_obs instead! |
loglike(params) |
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loglikeobs(params) |
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nloglike(params) |
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nloglikeobs(params) |
Loglikelihood of Poisson model |
predict(params[, exog]) |
After a model has been fit predict returns the fitted values. |
reduceparams(params) |
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score(params) |
Gradient of log-likelihood evaluated at params |
score_obs(params, **kwds) |
Jacobian/Gradient of log-likelihood evaluated at params for each observation. |
3.7.1.1.3.2. Attributes¶
endog_names |
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exog_names |