statsmodels.tsa.ar_model.ARResults.initialize ARResults.initialize(model, params, **kwd)

statsmodels.tsa.stattools.pacf_yw statsmodels.tsa.stattools.pacf_yw(x, nlags=40, method='unbiased') [source] Partial autocorrelation estimated with non-recursive yule_walker Parameters: x : 1d array observations of time series for which pacf is calculated nlags : int largest lag for which pacf is returned method : ?unbiased? (default) or ?mle? method for the autocovariance calculations in yule walker Returns: pacf : 1d array partial autocorrelations, maxlag+1 elements Notes This

statsmodels.tsa.stattools.ccf statsmodels.tsa.stattools.ccf(x, y, unbiased=True) [source] cross-correlation function for 1d Parameters: x, y : arrays time series data unbiased : boolean if True, then denominators for autocovariance is n-k, otherwise n Returns: ccf : array cross-correlation function of x and y Notes This is based np.correlate which does full convolution. For very long time series it is recommended to use fft convolution instead. If unbiased is true, the denominator

statsmodels.stats.power.FTestAnovaPower class statsmodels.stats.power.FTestAnovaPower(**kwds) [source] Statistical Power calculations F-test for one factor balanced ANOVA Methods plot_power([dep_var, nobs, effect_size, ...]) plot power with number of observations or effect size on x-axis power(effect_size, nobs, alpha[, k_groups]) Calculate the power of a F-test for one factor ANOVA. solve_power([effect_size, nobs, alpha, ...]) solve for any one parameter of the power of a F-test

statsmodels.regression.mixed_linear_model.MixedLM.hessian_full MixedLM.hessian_full(params) [source] Calculates the Hessian matrix for the mixed effects model with respect to the parameterization in which the covariance matrix is represented directly (without square-root transformation). Parameters: params : MixedLMParams or array-like The model parameters at which the Hessian is calculated. If array-like, must contain the packed parameters in a form that is compatible with this model inst

statsmodels.sandbox.distributions.extras.ACSkewT_gen.stats ACSkewT_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 moments to

statsmodels.sandbox.distributions.extras.ACSkewT_gen.ppf ACSkewT_gen.ppf(q, *args, **kwds) Percent point function (inverse of cdf) at q of the given RV. Parameters: q : array_like lower tail probability 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: x : array_like quan

statsmodels.genmod.families.links.Logit.inverse Logit.inverse(z) [source] Inverse of the logit transform Parameters: z : array-like The value of the logit transform at p Returns: p : array Probabilities Notes g^(-1)(z) = exp(z)/(1+exp(z))

statsmodels.regression.linear_model.RegressionResults.eigenvals static RegressionResults.eigenvals() [source] Return eigenvalues sorted in decreasing order.

statsmodels.stats.weightstats.DescrStatsW.sumsquares static DescrStatsW.sumsquares() [source] weighted sum of squares of demeaned data