statsmodels.genmod.cov_struct.Nested.update Nested.update(params) [source] Updates the association parameter values based on the current regression coefficients. Parameters: params : array-like Working values for the regression parameters.

statsmodels.tools.eval_measures.medianbias statsmodels.tools.eval_measures.medianbias(x1, x2, axis=0) [source] median bias, median error Parameters: x1, x2 : array_like The performance measure depends on the difference between these two arrays. axis : int axis along which the summary statistic is calculated Returns: medianbias : ndarray or float median bias, or median difference along given axis. Notes If x1 and x2 have different shapes, then they need to broadcast. This uses numpy

statsmodels.sandbox.distributions.extras.NormExpan_gen.sf NormExpan_gen.sf(x, *args, **kwds) Survival function (1-cdf) at x of the given RV. Parameters: x : array_like quantiles 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: sf : array_like Survival function evaluated

statsmodels.tsa.arima_model.ARIMAResults.save ARIMAResults.save(fname, remove_data=False) save a pickle of this instance Parameters: fname : string or filehandle fname can be a string to a file path or filename, or a filehandle. remove_data : bool If False (default), then the instance is pickled without changes. If True, then all arrays with length nobs are set to None before pickling. See the remove_data method. In some cases not all arrays will be set to None. Notes If remove_data is

statsmodels.tsa.arima_model.ARIMAResults.load classmethod ARIMAResults.load(fname) load a pickle, (class method) Parameters: fname : string or filehandle fname can be a string to a file path or filename, or a filehandle. Returns: unpickled instance :

statsmodels.sandbox.distributions.extras.ACSkewT_gen.rvs ACSkewT_gen.rvs(*args, **kwds) Random variates of given type. 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). size : int or tuple of ints, optional Defining number of random variates (default=1). Returns: rvs

statsmodels.stats.power.TTestIndPower.power TTestIndPower.power(effect_size, nobs1, alpha, ratio=1, df=None, alternative='two-sided') [source] Calculate the power of a t-test for two independent sample Parameters: effect_size : float standardized effect size, difference between the two means divided by the standard deviation. effect_size has to be positive. nobs1 : int or float number of observations of sample 1. The number of observations of sample two is ratio times the size of sample

statsmodels.robust.norms.AndrewWave.rho AndrewWave.rho(z) [source] The robust criterion function for Andrew?s wave. Parameters: z : array-like 1d array Returns: rho : array rho(z) = a*(1-cos(z/a)) for |z| <= a*pi rho(z) = 2*a for |z| > a*pi

statsmodels.tsa.kalmanf.kalmanfilter.KalmanFilter.T classmethod KalmanFilter.T(params, r, k, p) [source] The coefficient matrix for the state vector in the state equation. Its dimension is r+k x r+k. Parameters: r : int In the context of the ARMA model r is max(p,q+1) where p is the AR order and q is the MA order. k : int The number of exogenous variables in the ARMA model, including the constant if appropriate. p : int The AR coefficient in an ARMA model. References Durbin and Koopm

statsmodels.stats.power.NormalIndPower.power NormalIndPower.power(effect_size, nobs1, alpha, ratio=1, alternative='two-sided') [source] Calculate the power of a t-test for two independent sample Parameters: effect_size : float standardized effect size, difference between the two means divided by the standard deviation. effect size has to be positive. nobs1 : int or float number of observations of sample 1. The number of observations of sample two is ratio times the size of sample 1, i.e.