statsmodels.sandbox.tsa.fftarma.ArmaFft.spdshift ArmaFft.spdshift(n) [source] power spectral density using fftshift currently returns two-sided according to fft frequencies, use first half

statsmodels.regression.mixed_linear_model.MixedLM.fit_regularized MixedLM.fit_regularized(start_params=None, method='l1', alpha=0, ceps=0.0001, ptol=1e-06, maxit=200, **fit_kwargs) [source] Fit a model in which the fixed effects parameters are penalized. The dependence parameters are held fixed at their estimated values in the unpenalized model. Parameters: method : string of Penalty object Method for regularization. If a string, must be ?l1?. alpha : array-like Scalar or vector of penal

statsmodels.discrete.discrete_model.MultinomialResults.fittedvalues static MultinomialResults.fittedvalues()

statsmodels.sandbox.stats.multicomp.TukeyHSDResults.plot_simultaneous TukeyHSDResults.plot_simultaneous(comparison_name=None, ax=None, figsize=(10, 6), xlabel=None, ylabel=None) [source] Plot a universal confidence interval of each group mean Visiualize significant differences in a plot with one confidence interval per group instead of all pairwise confidence intervals. Parameters: comparison_name : string, optional if provided, plot_intervals will color code all groups that are significan

statsmodels.genmod.families.family.Gaussian.deviance Gaussian.deviance(endog, mu, scale=1.0) [source] Gaussian deviance function Parameters: endog : array-like Endogenous response variable mu : array-like Fitted mean response variable scale : float, optional An optional scale argument Returns: deviance : float The deviance function at (endog,mu) as defined below. Notes deviance = sum((endog-mu)**2)

statsmodels.iolib.table.SimpleTable.as_html SimpleTable.as_html(**fmt_dict) [source] Return string. This is the default formatter for HTML tables. An HTML table formatter must accept as arguments a table and a format dictionary.

statsmodels.miscmodels.count.PoissonGMLE.jac PoissonGMLE.jac(*args, **kwds) jac is deprecated, use score_obs instead! Use score_obs method. jac will be removed in 0.7. Jacobian/Gradient of log-likelihood evaluated at params for each observation.

statsmodels.tsa.arima_model.ARIMA.loglike ARIMA.loglike(params, set_sigma2=True) Compute the log-likelihood for ARMA(p,q) model Notes Likelihood used depends on the method set in fit

statsmodels.tsa.arima_model.ARMA.information ARMA.information(params) Fisher information matrix of model Returns -Hessian of loglike evaluated at params.

statsmodels.genmod.families.family.Family.weights Family.weights(mu) [source] 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))