statsmodels.genmod.cov_struct.Nested.initialize Nested.initialize(model) [source] Called on the first call to update ilabels is a list of n_i x n_i matrices containing integer labels that correspond to specific correlation parameters. Two elements of ilabels[i] with the same label share identical variance components. designx is a matrix, with each row containing dummy variables indicating which variance components are associated with the corresponding element of QY.

statsmodels.sandbox.regression.gmm.GMMResults.predict GMMResults.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 data structu

statsmodels.genmod.generalized_linear_model.GLMResults.predict GLMResults.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 dat

statsmodels.genmod.generalized_linear_model.GLMResults.save GLMResults.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 re

statsmodels.duration.hazard_regression.PHRegResults.remove_data PHRegResults.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 an attrib

statsmodels.genmod.generalized_linear_model.GLMResults.summary GLMResults.summary(yname=None, xname=None, title=None, alpha=0.05) [source] Summarize the Regression Results Parameters: yname : string, optional Default is y xname : list of strings, optional Default is var_## for ## in p the number of regressors title : string, optional Title for the top table. If not None, then this replaces the default title alpha : float significance level for the confidence intervals Returns: smr

statsmodels.sandbox.regression.try_catdata.groupstatsbin statsmodels.sandbox.regression.try_catdata.groupstatsbin(factors, values) [source] uses np.bincount, assumes factors/labels are integers

statsmodels.genmod.families.family.InverseGaussian class statsmodels.genmod.families.family.InverseGaussian(link=) [source] InverseGaussian exponential family. Parameters: link : a link instance, optional The default link for the inverse Gaussian family is the inverse squared link. Available links are inverse_squared, inverse, log, and identity. See statsmodels.family.links for more information. See also statsmodels.genmod.families.family.Family, Link Functions Notes The inverse Guassi

statsmodels.graphics.gofplots.ProbPlot.qqplot ProbPlot.qqplot(xlabel=None, ylabel=None, line=None, other=None, ax=None, **plotkwargs) [source] Q-Q plot of the quantiles of x versus the quantiles/ppf of a distribution or the quantiles of another ProbPlot instance. Parameters: xlabel, ylabel : str or None, optional User-provided lables for the x-axis and y-axis. If None (default), other values are used depending on the status of the kwarg other. line : str {?45?, ?s?, ?r?, q?} or None, opti

statsmodels.tsa.stattools.grangercausalitytests statsmodels.tsa.stattools.grangercausalitytests(x, maxlag, addconst=True, verbose=True) [source] four tests for granger non causality of 2 timeseries all four tests give similar results params_ftest and ssr_ftest are equivalent based on F test which is identical to lmtest:grangertest in R Parameters: x : array, 2d, (nobs,2) data for test whether the time series in the second column Granger causes the time series in the first column maxlag :