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

statsmodels.genmod.families.links.Logit.deriv Logit.deriv(p) [source] Derivative of the logit transform Parameters: p: array-like : Probabilities Returns: g?(p) : array Value of the derivative of logit transform at p Notes g?(p) = 1 / (p * (1 - p)) Alias for Logit: logit = Logit()

statsmodels.genmod.families.family.Binomial.resid_anscombe Binomial.resid_anscombe(endog, mu) [source] The Anscombe residuals Parameters: endog : array-like Endogenous response variable mu : array-like Fitted mean response variable Returns: resid_anscombe : array The Anscombe residuals as defined below. Notes sqrt(n)*(cox_snell(endog)-cox_snell(mu))/(mu**(1/6.)*(1-mu)**(1/6.)) where cox_snell is defined as cox_snell(x) = betainc(2/3., 2/3., x)*betainc(2/3.,2/3.) where betainc is th

statsmodels.regression.linear_model.yule_walker statsmodels.regression.linear_model.yule_walker(X, order=1, method='unbiased', df=None, inv=False, demean=True) [source] Estimate AR(p) parameters from a sequence X using Yule-Walker equation. Unbiased or maximum-likelihood estimator (mle) See, for example: http://en.wikipedia.org/wiki/Autoregressive_moving_average_model Parameters: X : array-like 1d array order : integer, optional The order of the autoregressive process. Default is 1. met

statsmodels.sandbox.distributions.transformed.TransfTwo_gen.stats TransfTwo_gen.stats(*args, **kwds) Some statistics of the given RV Parameters: arg1, arg2, arg3,... : array_like The shape parameter(s) for the distribution (see docstring of the instance object for more information) loc : array_like, optional location parameter (default=0) scale : array_like, optional (discrete RVs only) scale parameter (default=1) moments : str, optional composed of letters [?mvsk?] defining which mo

statsmodels.genmod.families.links.probit.deriv probit.deriv(p) Derivative of CDF link Parameters: p : array-like mean parameters Returns: g?(p) : array The derivative of CDF transform at p Notes g?(p) = 1./ dbn.pdf(dbn.ppf(p))

statsmodels.nonparametric.kernel_density.KDEMultivariateConditional.pdf KDEMultivariateConditional.pdf(endog_predict=None, exog_predict=None) [source] Evaluate the probability density function. Parameters: endog_predict: array_like, optional : Evaluation data for the dependent variables. If unspecified, the training data is used. exog_predict: array_like, optional : Evaluation data for the independent variables. Returns: pdf: array_like : The value of the probability density at endog

statsmodels.discrete.discrete_model.MNLogit.score_obs MNLogit.score_obs(params) [source] Jacobian matrix for multinomial logit model log-likelihood Parameters: params : array The parameters of the multinomial logit model. Returns: jac : ndarray, (nobs, k_vars*(J-1)) The derivative of the loglikelihood for each observation evaluated at params . Notes for , for observations In the multinomial model the score vector is K x (J-1) but is returned as a flattened array. The Jacobian has

statsmodels.genmod.families.links.CDFLink.deriv CDFLink.deriv(p) [source] Derivative of CDF link Parameters: p : array-like mean parameters Returns: g?(p) : array The derivative of CDF transform at p Notes g?(p) = 1./ dbn.pdf(dbn.ppf(p))

statsmodels.genmod.families.links.probit.inverse_deriv probit.inverse_deriv(z) Derivative of the inverse of the CDF transformation link function Parameters: z : array The inverse of the link function at p Returns: The value of the derivative of the inverse of the logit function :