Negative Binomial Model for count data
Parameters: | endog : array-like
exog : array-like
loglike_method : string
offset : array_like
exposure : array_like
missing : str
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References
References:
Attributes
endog | array | A reference to the endogenous response variable |
exog | array | A reference to the exogenous design. |
Methods
cdf(X) | The cumulative distribution function of the model. |
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_regularized([start_params, method, ...]) | |
from_formula(formula, data[, subset, drop_cols]) | Create a Model from a formula and dataframe. |
hessian(params) | The Hessian matrix of the model |
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 for negative binomial model |
pdf(X) | The probability density (mass) function of the model. |
predict(params[, exog, exposure, offset, linear]) | Predict response variable of a count model given exogenous variables. |
score(params) | Score vector of model. |
score_obs(params) |
Methods
cdf(X) | The cumulative distribution function of the model. |
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_regularized([start_params, method, ...]) | |
from_formula(formula, data[, subset, drop_cols]) | Create a Model from a formula and dataframe. |
hessian(params) | The Hessian matrix of the model |
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 for negative binomial model |
pdf(X) | The probability density (mass) function of the model. |
predict(params[, exog, exposure, offset, linear]) | Predict response variable of a count model given exogenous variables. |
score(params) | Score vector of model. |
score_obs(params) |
Attributes
endog_names | Names of endogenous variables |
exog_names | Names of exogenous variables |