statsmodels.robust.norms.HuberT class statsmodels.robust.norms.HuberT(t=1.345) [source] Huber?s T for M estimation. Parameters: t : float, optional The tuning constant for Huber?s t function. The default value is 1.345. See also statsmodels.robust.norms.RobustNorm Methods psi(z) The psi function for Huber?s t estimator psi_deriv(z) The derivative of Huber?s t psi function rho(z) The robust criterion function for Huber?s t. weights(z) Huber?s t weighting function for the IRLS algori

statsmodels.genmod.cov_struct.Autoregressive.summary Autoregressive.summary() [source]

statsmodels.tsa.arima_model.ARMAResults.initialize ARMAResults.initialize(model, params, **kwd)

statsmodels.genmod.generalized_linear_model.GLM.initialize GLM.initialize() [source] Initialize a generalized linear model.

statsmodels.sandbox.regression.try_ols_anova.data2proddummy statsmodels.sandbox.regression.try_ols_anova.data2proddummy(x) [source] creates product dummy variables from 2 columns of 2d array drops last dummy variable, but not from each category singular with simple dummy variable but not with constant quickly written, no safeguards

statsmodels.discrete.discrete_model.DiscreteResults.normalized_cov_params DiscreteResults.normalized_cov_params()

statsmodels.regression.linear_model.GLSAR.information GLSAR.information(params) Fisher information matrix of model Returns -Hessian of loglike evaluated at params.

statsmodels.discrete.discrete_model.LogitResults.get_margeff LogitResults.get_margeff(at='overall', method='dydx', atexog=None, dummy=False, count=False) Get marginal effects of the fitted model. Parameters: at : str, optional Options are: ?overall?, The average of the marginal effects at each observation. ?mean?, The marginal effects at the mean of each regressor. ?median?, The marginal effects at the median of each regressor. ?zero?, The marginal effects at zero for each regressor. ?all?

statsmodels.discrete.discrete_model.LogitResults.pred_table LogitResults.pred_table(threshold=0.5) Prediction table Parameters: threshold : scalar Number between 0 and 1. Threshold above which a prediction is considered 1 and below which a prediction is considered 0. Notes pred_table[i,j] refers to the number of times ?i? was observed and the model predicted ?j?. Correct predictions are along the diagonal.

statsmodels.sandbox.distributions.transformed.LogTransf_gen.std LogTransf_gen.std(*args, **kwds) Standard deviation of the distribution. 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 scale parameter (default=1) Returns: std : float standard deviation of the distribution