statsmodels.tsa.arima_model.ARIMAResults.maroots static ARIMAResults.maroots()

statsmodels.robust.robust_linear_model.RLMResults.bse static RLMResults.bse() [source]

statsmodels.tsa.vector_ar.var_model.VARResults.plot VARResults.plot() [source] Plot input time series

statsmodels.discrete.discrete_model.Probit.score Probit.score(params) [source] Probit model score (gradient) vector Parameters: params : array-like The parameters of the model Returns: score : ndarray, 1-D The score vector of the model, i.e. the first derivative of the loglikelihood function, evaluated at params Notes Where . This simplification comes from the fact that the normal distribution is symmetric.

statsmodels.discrete.discrete_model.Probit.cdf Probit.cdf(X) [source] Probit (Normal) cumulative distribution function Parameters: X : array-like The linear predictor of the model (XB). Returns: cdf : ndarray The cdf evaluated at X. Notes This function is just an alias for scipy.stats.norm.cdf

statsmodels.discrete.discrete_model.Poisson.jac Poisson.jac(*args, **kwds) jac is deprecated, use score_obs instead! Use score_obs method. jac will be removed in 0.7 Poisson model Jacobian of the log-likelihood for each observation Parameters: params : array-like The parameters of the model Returns: score : ndarray (nobs, k_vars) The score vector of the model evaluated at params Notes for observations where the loglinear model is assumed

statsmodels.regression.linear_model.GLSAR.hessian GLSAR.hessian(params) The Hessian matrix of the model

statsmodels.discrete.discrete_model.Probit.loglike Probit.loglike(params) [source] Log-likelihood of probit model (i.e., the normal distribution). Parameters: params : array-like The parameters of the model. Returns: loglike : float The log-likelihood function of the model evaluated at params. See notes. Notes Where . This simplification comes from the fact that the normal distribution is symmetric.

statsmodels.discrete.discrete_model.Poisson.cdf Poisson.cdf(X) [source] Poisson model cumulative distribution function Parameters: X : array-like X is the linear predictor of the model. See notes. Returns: The value of the Poisson CDF at each point. : Notes The CDF is defined as where assumes the loglinear model. I.e., The parameter X is in the above formula.

statsmodels.discrete.discrete_model.MultinomialResults.aic static MultinomialResults.aic() [source]