statsmodels.genmod.generalized_linear_model.GLMResults.resid_working static GLMResults.resid_working() [source]

statsmodels.sandbox.distributions.transformed.TransfTwo_gen.ppf TransfTwo_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_l

statsmodels.sandbox.stats.multicomp.varcorrection_pairs_unbalanced statsmodels.sandbox.stats.multicomp.varcorrection_pairs_unbalanced(nobs_all, srange=False) [source] correction factor for variance with unequal sample sizes for all pairs this is just a harmonic mean Parameters: nobs_all : array_like The number of observations for each sample srange : bool if true, then the correction is divided by 2 for the variance of the studentized range statistic Returns: correction : array Corre

statsmodels.duration.hazard_regression.PHReg.fit_regularized PHReg.fit_regularized(method='coord_descent', maxiter=100, alpha=0.0, L1_wt=1.0, start_params=None, cnvrg_tol=1e-07, zero_tol=1e-08, **kwargs) [source] Return a regularized fit to a linear regression model. Parameters: method : : Only the coordinate descent algorithm is implemented. maxiter : integer The maximum number of iteration cycles (an iteration cycle involves running coordinate descent on all variables). alpha : scalar

statsmodels.tsa.stattools.acovf statsmodels.tsa.stattools.acovf(x, unbiased=False, demean=True, fft=False) [source] Autocovariance for 1D Parameters: x : array Time series data. Must be 1d. unbiased : bool If True, then denominators is n-k, otherwise n demean : bool If True, then subtract the mean x from each element of x fft : bool If True, use FFT convolution. This method should be preferred for long time series. Returns: acovf : array autocovariance function

statsmodels.discrete.discrete_model.BinaryResults.summary2 BinaryResults.summary2(yname=None, xname=None, title=None, alpha=0.05, float_format='%.4f') Experimental function to summarize regression results Parameters: xname : List of strings of length equal to the number of parameters Names of the independent variables (optional) yname : string Name of the dependent variable (optional) title : string, optional Title for the top table. If not None, then this replaces the default title a

statsmodels.sandbox.regression.gmm.GMM.gradient_momcond GMM.gradient_momcond(params, epsilon=0.0001, centered=True) [source] gradient of moment conditions Parameters: params : ndarray parameter at which the moment conditions are evaluated epsilon : float stepsize for finite difference calculation centered : bool This refers to the finite difference calculation. If centered is true, then the centered finite difference calculation is used. Otherwise the one-sided forward differences are

statsmodels.sandbox.tsa.fftarma.ArmaFft.arma2ar ArmaFft.arma2ar(nobs=None)

statsmodels.nonparametric.kernel_density.KDEMultivariate.imse KDEMultivariate.imse(bw) [source] Returns the Integrated Mean Square Error for the unconditional KDE. Parameters: bw: array_like : The bandwidth parameter(s). Returns: CV: float : The cross-validation objective function. Notes See p. 27 in [R6] for details on how to handle the multivariate estimation with mixed data types see p.6 in [R7]. The formula for the cross-validation objective function is: Where is the multivar

statsmodels.tsa.kalmanf.kalmanfilter.KalmanFilter.Z classmethod KalmanFilter.Z(r) [source] Returns the Z selector matrix in the observation equation. 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. Notes Currently only returns a 1 x r vector [1,0,0,...0]. Will need to be generalized when the Kalman Filter becomes more flexible.