Poisson model for count data
Parameters: | endog : array-like
exog : array-like
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Attributes
endog | array | A reference to the endogenous response variable |
exog | array | A reference to the exogenous design. |
Methods
cdf(X) | Poisson model cumulative distribution function |
fit([start_params, method, maxiter, ...]) | Fit the model using maximum likelihood. |
hessian(params) | Poisson model Hessian matrix of the loglikelihood |
information(params) | Fisher information matrix of model |
initialize() | Initialize is called by statsmodels.model.LikelihoodModel.__init__ and should contain any preprocessing that needs to be done for a model. |
jac(params) | Poisson model Jacobian of the log-likelihood for each observation |
loglike(params) | Loglikelihood of Poisson model |
loglikeobs(params) | Loglikelihood for observations of Poisson model |
pdf(X) | Poisson model probability mass function |
predict(params[, exog, exposure, offset, linear]) | Predict response variable of a count model given exogenous variables. |
score(params) | Poisson model score (gradient) vector of the log-likelihood |
Attributes
endog_names | |
exog_names |