statsmodels.robust.robust_linear_model.RLMResults.save RLMResults.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_

statsmodels.genmod.generalized_linear_model.GLM.from_formula classmethod GLM.from_formula(formula, data, subset=None, *args, **kwargs) Create a Model from a formula and dataframe. Parameters: formula : str or generic Formula object The formula specifying the model data : array-like The data for the model. See Notes. subset : array-like An array-like object of booleans, integers, or index values that indicate the subset of df to use in the model. Assumes df is a pandas.DataFrame args :

statsmodels.robust.robust_linear_model.RLMResults.load classmethod RLMResults.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.tsa.fftarma.ArmaFft.spdpoly ArmaFft.spdpoly(w, nma=50) [source] spectral density from MA polynomial representation for ARMA process References Cochrane, section 8.3.3

statsmodels.genmod.families.family.Binomial.resid_anscombe Binomial.resid_anscombe(endog, mu) [source] The Anscombe residuals Parameters: endog : array-like Endogenous response variable mu : array-like Fitted mean response variable Returns: resid_anscombe : array The Anscombe residuals as defined below. Notes sqrt(n)*(cox_snell(endog)-cox_snell(mu))/(mu**(1/6.)*(1-mu)**(1/6.)) where cox_snell is defined as cox_snell(x) = betainc(2/3., 2/3., x)*betainc(2/3.,2/3.) where betainc is th

statsmodels.genmod.families.family.Poisson.weights Poisson.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.sandbox.regression.gmm.GMM.fitgmm_cu GMM.fitgmm_cu(start, optim_method='bfgs', optim_args=None) [source] 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

statsmodels.genmod.families.family.InverseGaussian.starting_mu InverseGaussian.starting_mu(y) Starting value for mu in the IRLS algorithm. Parameters: y : array The untransformed response variable. Returns: mu_0 : array The first guess on the transformed response variable. Notes Only the Binomial family takes a different initial value.

statsmodels.regression.linear_model.OLSResults.conf_int OLSResults.conf_int(alpha=0.05, cols=None) Returns the confidence interval of the fitted parameters. Parameters: alpha : float, optional The alpha level for the confidence interval. ie., The default alpha = .05 returns a 95% confidence interval. cols : array-like, optional cols specifies which confidence intervals to return Notes The confidence interval is based on Student?s t-distribution.

statsmodels.regression.linear_model.OLSResults.conf_int_el OLSResults.conf_int_el(param_num, sig=0.05, upper_bound=None, lower_bound=None, method='nm', stochastic_exog=1) [source] Computes the confidence interval for the parameter given by param_num using Empirical Likelihood Parameters: param_num : float The parameter for which the confidence interval is desired sig : float The significance level. Default is .05 upper_bound : float The maximum value the upper limit can be. Default is