statsmodels.robust.norms.TrimmedMean.weights TrimmedMean.weights(z) [source] Least trimmed mean weighting function for the IRLS algorithm The psi function scaled by z Parameters: z : array-like 1d array Returns: weights : array weights(z) = 1 for |z| <= c weights(z) = 0 for |z| > c

statsmodels.discrete.discrete_model.BinaryModel.predict BinaryModel.predict(params, exog=None, linear=False) [source] Predict response variable of a model given exogenous variables. Parameters: params : array-like Fitted parameters of the model. exog : array-like 1d or 2d array of exogenous values. If not supplied, the whole exog attribute of the model is used. linear : bool, optional If True, returns the linear predictor dot(exog,params). Else, returns the value of the cdf at the line

statsmodels.discrete.discrete_model.BinaryResults.save BinaryResults.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 remo

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.robust.robust_linear_model.RLM.predict RLM.predict(params, exog=None) [source] Return linear predicted values from a design matrix. Parameters: params : array-like, optional after fit has been called Parameters of a linear model exog : array-like, optional. Design / exogenous data. Model exog is used if None. Returns: An array of fitted values : Notes If the model as not yet been fit, params is not optional.

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

statsmodels.tools.tools.fullrank statsmodels.tools.tools.fullrank(X, r=None) [source] Return a matrix whose column span is the same as X. If the rank of X is known it can be specified as r ? no check is made to ensure that this really is the rank of X.