statsmodels.discrete.discrete_model.Probit.score Probit.score(params) [source] Probit model score (gradient) vector Parameters: params : array-like The parameters of the model Returns: score : ndarray, 1-D The score vector of the model, i.e. the first derivative of the loglikelihood function, evaluated at params Notes Where . This simplification comes from the fact that the normal distribution is symmetric.

statsmodels.iolib.table.csv2st statsmodels.iolib.table.csv2st(csvfile, headers=False, stubs=False, title=None) [source] Return SimpleTable instance, created from the data in csvfile, which is in comma separated values format. The first row may contain headers: set headers=True. The first column may contain stubs: set stubs=True. Can also supply headers and stubs as tuples of strings.

statsmodels.regression.quantile_regression.QuantRegResults.nobs static QuantRegResults.nobs()

statsmodels.discrete.discrete_model.Poisson.hessian Poisson.hessian(params) [source] Poisson model Hessian matrix of the loglikelihood Parameters: params : array-like The parameters of the model Returns: hess : ndarray, (k_vars, k_vars) The Hessian, second derivative of loglikelihood function, evaluated at params Notes where the loglinear model is assumed

statsmodels.tsa.ar_model.ARResults.initialize ARResults.initialize(model, params, **kwd)

statsmodels.sandbox.tsa.fftarma.ArmaFft.filter2 ArmaFft.filter2(x, pad=0) [source] filter a time series using fftconvolve3 with ARMA filter padding of x currently works only if x is 1d in example it produces same observations at beginning as lfilter even without padding. TODO: this returns 1 additional observation at the end

statsmodels.sandbox.regression.gmm.IVRegressionResults.fittedvalues static IVRegressionResults.fittedvalues()

statsmodels.sandbox.regression.gmm.IVRegressionResults.aic static IVRegressionResults.aic()

statsmodels.sandbox.regression.gmm.IVRegressionResults.compare_lm_test IVRegressionResults.compare_lm_test(restricted, demean=True, use_lr=False) Use Lagrange Multiplier test to test whether restricted model is correct Parameters: restricted : Result instance The restricted model is assumed to be nested in the current model. The result instance of the restricted model is required to have two attributes, residual sum of squares, ssr, residual degrees of freedom, df_resid. demean : bool Fl

statsmodels.genmod.generalized_estimating_equations.GEEResults.resid_centered static GEEResults.resid_centered() [source] Returns the residuals centered within each group.