statsmodels.sandbox.tools.tools_pca.pca statsmodels.sandbox.tools.tools_pca.pca(data, keepdim=0, normalize=0, demean=True) [source] principal components with eigenvector decomposition similar to princomp in matlab Parameters: data : ndarray, 2d data with observations by rows and variables in columns keepdim : integer number of eigenvectors to keep if keepdim is zero, then all eigenvectors are included normalize : boolean if true, then eigenvectors are normalized by sqrt of eigenvalues

statsmodels.discrete.discrete_model.MultinomialResults.pred_table MultinomialResults.pred_table() [source] Returns the J x J prediction table. 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.stats.diagnostic.CompareJ class statsmodels.stats.diagnostic.CompareJ J-Test for comparing non-nested models Parameters: results_x : Result instance result instance of first model results_z : Result instance result instance of second model attach : bool From description in Greene, section 8.3.3 : produces correct results for Example 8.3, Greene - not checked yet : #currently an exception, but I don?t have clean reload in python session : check what results should be attached

statsmodels.sandbox.regression.gmm.IVGMMResults.predict IVGMMResults.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 str

statsmodels.regression.linear_model.RegressionResults.mse_resid static RegressionResults.mse_resid() [source]

statsmodels.duration.hazard_regression.PHRegResults.pvalues static PHRegResults.pvalues()

statsmodels.tsa.vector_ar.irf.IRAnalysis.cum_errband_mc IRAnalysis.cum_errband_mc(orth=False, repl=1000, signif=0.05, seed=None, burn=100) [source] IRF Monte Carlo integrated error bands of cumulative effect

statsmodels.duration.hazard_regression.PHRegResults.llf static PHRegResults.llf()

statsmodels.genmod.generalized_estimating_equations.GEEResults.centered_resid static GEEResults.centered_resid() Returns the residuals centered within each group.

statsmodels.genmod.generalized_estimating_equations.GEEResults.resid_centered_split static GEEResults.resid_centered_split() [source] Returns the residuals centered within each group. The residuals are returned as a list of arrays containing the centered residuals for each cluster.