statsmodels.discrete.truncated_model.TruncatedLFPoisson

class statsmodels.discrete.truncated_model.TruncatedLFPoisson(endog, exog, offset=None, exposure=None, truncation=0, missing='none', **kwargs)

Truncated Poisson model for count data

Added in version 0.14.0.

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.

missingstr

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’.

Attributes:

endogarray

A reference to the endogenous response variable

exogarray

A reference to the exogenous design.

truncationint, optional

Truncation parameter specify truncation point out of the support of the distribution. pmf(k) = 0 for k <= truncation

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 the model using maximum likelihood.
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)	Generic Truncated model Hessian matrix of the log-likelihood.
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.
loglike(params)	Log-likelihood of Generic Truncated model.
loglikeobs(params)	Log-likelihood for observations of Generic Truncated model.
pdf(X)	The probability density (mass) function of the model.
predict(params[, exog, exposure, offset, ...])	Predict response variable or other statistic given exogenous variables.
score(params)	Generic Truncated model score (gradient) vector of the log-likelihood.
score_obs(params)	Generic Truncated model score (gradient) vector of the log-likelihood.

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 the model using maximum likelihood.
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)	Generic Truncated model Hessian matrix of the log-likelihood.
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.
loglike(params)	Log-likelihood of Generic Truncated model.
loglikeobs(params)	Log-likelihood for observations of Generic Truncated model.
pdf(X)	The probability density (mass) function of the model.
predict(params[, exog, exposure, offset, ...])	Predict response variable or other statistic given exogenous variables.
score(params)	Generic Truncated model score (gradient) vector of the log-likelihood.
score_obs(params)	Generic Truncated model score (gradient) vector of the log-likelihood.

Properties

endog_names	Names of endogenous variables
exog_names	Names of exogenous variables