statsmodels.sandbox.distributions.extras.SkewNorm_gen.mean SkewNorm_gen.mean(*args, **kwds) Mean of the distribution 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 scale parameter (default=1) Returns: mean : float the mean of the distribution

statsmodels.stats.weightstats.ztost statsmodels.stats.weightstats.ztost(x1, low, upp, x2=None, usevar='pooled', ddof=1.0) [source] Equivalence test based on normal distribution Parameters: x1 : array_like one sample or first sample for 2 independent samples low, upp : float equivalence interval low < m1 - m2 < upp x1 : array_like or None second sample for 2 independent samples test. If None, then a one-sample test is performed. usevar : string, ?pooled? If pooled, then the stan

statsmodels.discrete.discrete_model.LogitResults.resid_dev static LogitResults.resid_dev() Deviance residuals Notes Deviance residuals are defined where and is the total number of observations sharing the covariate pattern . For now is always set to 1.

statsmodels.genmod.generalized_linear_model.GLM.hessian GLM.hessian(params, scale=None, observed=True) [source] Hessian, second derivative of loglikelihood function Parameters: params : ndarray parameter at which Hessian is evaluated scale : None or float If scale is None, then the default scale will be calculated. Default scale is defined by self.scaletype and set in fit. If scale is not None, then it is used as a fixed scale. observed : bool If True, then the observed Hessian is retu

statsmodels.regression.linear_model.GLSAR.information GLSAR.information(params) Fisher information matrix of model Returns -Hessian of loglike evaluated at params.

statsmodels.genmod.families.family.Gaussian.weights Gaussian.weights(mu) Weights for IRLS steps Parameters: mu : array-like The transformed mean response variable in the exponential family Returns: w : array The weights for the IRLS steps Notes w = 1 / (link?(mu)**2 * variance(mu))

statsmodels.discrete.discrete_model.MNLogit.cdf MNLogit.cdf(X) [source] Multinomial logit cumulative distribution function. Parameters: X : array The linear predictor of the model XB. Returns: cdf : ndarray The cdf evaluated at X. Notes In the multinomial logit model. .. math:: frac{expleft(beta_{j}^{prime}x_{i}right)}{sum_{k=0}^{J}expleft(beta_{k}^{prime}x_{i}right)}

statsmodels.tsa.arima_model.ARIMAResults.predict ARIMAResults.predict(start=None, end=None, exog=None, typ='linear', dynamic=False) [source] ARIMA model in-sample and out-of-sample prediction Parameters: start : int, str, or datetime Zero-indexed observation number at which to start forecasting, ie., the first forecast is start. Can also be a date string to parse or a datetime type. end : int, str, or datetime Zero-indexed observation number at which to end forecasting, ie., the first fo

statsmodels.stats.weightstats.CompareMeans.ztost_ind CompareMeans.ztost_ind(low, upp, usevar='pooled') [source] test of equivalence for two independent samples, based on z-test Parameters: low, upp : float equivalence interval low < m1 - m2 < upp usevar : string, ?pooled? or ?unequal? If pooled, then the standard deviation of the samples is assumed to be the same. If unequal, then Welsh ttest with Satterthwait degrees of freedom is used Returns: pvalue : float pvalue of the non

statsmodels.miscmodels.tmodel.TLinearModel.predict TLinearModel.predict(params, exog=None) [source]