statsmodels.discrete.discrete_model.Poisson¶
-
class
statsmodels.discrete.discrete_model.Poisson(endog, exog, offset=None, exposure=None, missing='none', check_rank=True, **kwargs)[source]¶ Poisson Model
- Parameters
- endogarray_like
A 1-d endogenous response variable. The dependent variable.
- exogarray_like
A nobs x k array where nobs is the number of observations and k is the number of regressors. An intercept is not included by default and should be added by the user. See
statsmodels.tools.add_constant.- offsetarray_like
Offset is added to the linear prediction with coefficient equal to 1.
- exposurearray_like
Log(exposure) is added to the linear prediction with coefficient equal to 1. missing : str Available options are ‘none’, ‘drop’, and ‘raise’. If ‘none’, no nan checking is done. If ‘drop’, any observations with nans are dropped. If ‘raise’, an error is raised. Default is ‘none’.
- check_rankbool
Check exog rank to determine model degrees of freedom. Default is True. Setting to False reduces model initialization time when exog.shape[1] is large.
- Attributes
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, drop_cols])Create a Model from a formula and dataframe.
hessian(params)Poisson model Hessian matrix of the loglikelihood
hessian_factor(params)Poisson model Hessian factor
information(params)Fisher information matrix of model.
Initialize is called by statsmodels.model.LikelihoodModel.__init__ and should contain any preprocessing that needs to be done for a model.
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_factor(params)Poisson model score_factor for each observation
score_obs(params)Poisson model Jacobian of the log-likelihood for each observation
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, drop_cols])Create a Model from a formula and dataframe.
hessian(params)Poisson model Hessian matrix of the loglikelihood
hessian_factor(params)Poisson model Hessian factor
information(params)Fisher information matrix of model.
Initialize is called by statsmodels.model.LikelihoodModel.__init__ and should contain any preprocessing that needs to be done for a model.
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_factor(params)Poisson model score_factor for each observation
score_obs(params)Poisson model Jacobian of the log-likelihood for each observation
Properties
Names of endogenous variables.
Names of exogenous variables.