statsmodels.genmod.families.links.Power.deriv Power.deriv(p) [source] Derivative of the power transform Parameters: p : array-like Mean parameters Returns: g?(p) : array Derivative of power transform of p Notes g?(p) = power * p`**(`power - 1)
statsmodels.miscmodels.count.PoissonZiGMLE.score_obs PoissonZiGMLE.score_obs(params, **kwds) Jacobian/Gradient of log-likelihood evaluated at params for each observation.
statsmodels.miscmodels.count.PoissonZiGMLE.score PoissonZiGMLE.score(params) Gradient of log-likelihood evaluated at params
statsmodels.miscmodels.count.PoissonZiGMLE.reduceparams PoissonZiGMLE.reduceparams(params)
statsmodels.miscmodels.count.PoissonZiGMLE.predict PoissonZiGMLE.predict(params, exog=None, *args, **kwargs) After a model has been fit predict returns the fitted values. This is a placeholder intended to be overwritten by individual models.
statsmodels.miscmodels.count.PoissonZiGMLE.nloglikeobs PoissonZiGMLE.nloglikeobs(params) [source] Loglikelihood of Poisson model Parameters: params : array-like The parameters of the model. Returns: The log likelihood of the model evaluated at `params` : Notes
statsmodels.miscmodels.count.PoissonZiGMLE.nloglike PoissonZiGMLE.nloglike(params)
statsmodels.miscmodels.count.PoissonZiGMLE.loglikeobs PoissonZiGMLE.loglikeobs(params)
statsmodels.miscmodels.count.PoissonZiGMLE.loglike PoissonZiGMLE.loglike(params)
statsmodels.miscmodels.count.PoissonZiGMLE.jac PoissonZiGMLE.jac(*args, **kwds) jac is deprecated, use score_obs instead! Use score_obs method. jac will be removed in 0.7. Jacobian/Gradient of log-likelihood evaluated at params for each observation.
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