Poisson model for count data
Parameters: | endog : array-like
exog : array-like
offset : array_like
exposure : array_like
missing : str
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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 |
cov_params_func_l1(likelihood_model, xopt, ...) | Computes cov_params on a reduced parameter space corresponding to the nonzero parameters resulting from the l1 regularized fit. |
fit([start_params, method, maxiter, ...]) | Fit the model using maximum likelihood. |
fit_constrained(constraints[, start_params]) | fit the model subject to linear equality constraints |
fit_regularized([start_params, method, ...]) | Fit the model using a regularized maximum likelihood. |
from_formula(formula, data[, subset]) | Create a Model from a formula and dataframe. |
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(*args, **kwds) | jac is deprecated, use score_obs instead! |
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 |
score_obs(params) | Poisson model Jacobian of the log-likelihood for each observation |
Attributes
endog_names | |
exog_names |