statsmodels.sandbox.distributions.transformed.ExpTransf_gen.pdf ExpTransf_gen.pdf(x, *args, **kwds) Probability density function at x of the given RV. Parameters: x : array_like quantiles 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: pdf : ndarray Probability density

statsmodels.robust.norms.LeastSquares.psi LeastSquares.psi(z) [source] The psi function for the least squares estimator The analytic derivative of rho Parameters: z : array-like 1d array Returns: psi : array psi(z) = z

statsmodels.sandbox.distributions.extras.ACSkewT_gen.isf ACSkewT_gen.isf(q, *args, **kwds) Inverse survival function at q of the given RV. Parameters: q : array_like upper 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 : ndarray or scalar Quantile co

statsmodels.sandbox.distributions.extras.ACSkewT_gen.std ACSkewT_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

statsmodels.iolib.summary.Summary class statsmodels.iolib.summary.Summary [source] class to hold tables for result summary presentation Construction does not take any parameters. Tables and text can be added with the add_ methods. Attributes tables list of tables Contains the list of SimpleTable instances, horizontally concatenated tables are not saved separately. extra_txt string extra lines that are added to the text output, used for warnings and explanations. Methods add_extra_txt(etext)

statsmodels.sandbox.distributions.transformed.Transf_gen.sf Transf_gen.sf(x, *args, **kwds) Survival function (1-cdf) at x of the given RV. Parameters: x : array_like quantiles 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: sf : array_like Survival function evaluated a

statsmodels.discrete.discrete_model.BinaryResults.resid_pearson static BinaryResults.resid_pearson() [source] Pearson residuals Notes Pearson residuals are defined to be where and is the total number of observations sharing the covariate pattern . For now is always set to 1.

statsmodels.sandbox.distributions.transformed.ExpTransf_gen.mean ExpTransf_gen.mean(*args, **kwds) Mean 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: mean : float the mean of the distribution

statsmodels.sandbox.regression.gmm.IVRegressionResults.remove_data IVRegressionResults.remove_data() remove data arrays, all nobs arrays from result and model This reduces the size of the instance, so it can be pickled with less memory. Currently tested for use with predict from an unpickled results and model instance. Warning Since data and some intermediate results have been removed calculating new statistics that require them will raise exceptions. The exception will occur the first time

statsmodels.discrete.discrete_model.DiscreteResults.predict DiscreteResults.predict(exog=None, transform=True, *args, **kwargs) Call self.model.predict with self.params as the first argument. Parameters: exog : array-like, optional The values for which you want to predict. transform : bool, optional If the model was fit via a formula, do you want to pass exog through the formula. Default is True. E.g., if you fit a model y ~ log(x1) + log(x2), and transform is True, then you can pass a d