statsmodels.graphics.gofplots.ProbPlot.sorted_data static ProbPlot.sorted_data() [source]

statsmodels.sandbox.tsa.fftarma.ArmaFft.invertroots ArmaFft.invertroots(retnew=False) make MA polynomial invertible by inverting roots inside unit circle Parameters: retnew : boolean If False (default), then return the lag-polynomial as array. If True, then return a new instance with invertible MA-polynomial Returns: manew : array new invertible MA lag-polynomial, returned if retnew is false. wasinvertible : boolean True if the MA lag-polynomial was already invertible, returned if re

statsmodels.tsa.ar_model.ARResults.aic static ARResults.aic() [source]

statsmodels.robust.norms.TukeyBiweight.psi TukeyBiweight.psi(z) [source] The psi function for Tukey?s biweight estimator The analytic derivative of rho Parameters: z : array-like 1d array Returns: psi : array psi(z) = z*(1 - (z/c)**2)**2 for |z| <= R psi(z) = 0 for |z| > R

statsmodels.genmod.families.family.Gamma.loglike Gamma.loglike(endog, mu, scale=1.0) [source] Loglikelihood function for Gamma exponential family distribution. Parameters: endog : array-like Endogenous response variable mu : array-like Fitted mean response variable scale : float, optional The default is 1. Returns: llf : float The value of the loglikelihood function evaluated at (endog,mu,scale) as defined below. Notes llf = -1/scale * sum(endog/mu + log(mu) + (scale-1)*log(endog

statsmodels.tsa.vector_ar.var_model.VARResults.irf_errband_mc VARResults.irf_errband_mc(orth=False, repl=1000, T=10, signif=0.05, seed=None, burn=100, cum=False) [source] Compute Monte Carlo integrated error bands assuming normally distributed for impulse response functions Parameters: orth: bool, default False : Compute orthoganalized impulse response error bands repl: int : number of Monte Carlo replications to perform T: int, default 10 : number of impulse response periods signif:

statsmodels.tsa.arima_model.ARMA class statsmodels.tsa.arima_model.ARMA(endog, order, exog=None, dates=None, freq=None, missing='none') [source] Autoregressive Moving Average ARMA(p,q) Model Parameters: endog : array-like The endogenous variable. order : iterable The (p,q) order of the model for the number of AR parameters, differences, and MA parameters to use. exog : array-like, optional An optional arry of exogenous variables. This should not include a constant or trend. You can spe

statsmodels.discrete.discrete_model.LogitResults.prsquared static LogitResults.prsquared()

statsmodels.tsa.varma_process.VarmaPoly.vstackarma_minus1 VarmaPoly.vstackarma_minus1() [source] stack ar and lagpolynomial vertically in 2d array

statsmodels.sandbox.regression.gmm.LinearIVGMM.fitgmm_cu LinearIVGMM.fitgmm_cu(start, optim_method='bfgs', optim_args=None) estimate parameters using continuously updating GMM Parameters: start : array_like starting values for minimization Returns: paramest : array estimated parameters Notes todo: add fixed parameter option, not here ??? uses scipy.optimize.fmin