statsmodels.tools.numdiff.approx_fprime statsmodels.tools.numdiff.approx_fprime(x, f, epsilon=None, args=(), kwargs={}, centered=False) [source] Gradient of function, or Jacobian if function f returns 1d array Parameters: x : array parameters at which the derivative is evaluated f : function f(*((x,)+args), **kwargs) returning either one value or 1d array epsilon : float, optional Stepsize, if None, optimal stepsize is used. This is EPS**(1/2)*x for centered == False and EPS**(1/3)*x f

statsmodels.genmod.generalized_estimating_equations.GEEResults.conf_int GEEResults.conf_int(alpha=0.05, cols=None, cov_type=None) [source] Returns confidence intervals for the fitted parameters. Parameters: alpha : float, optional The alpha level for the confidence interval. i.e., The default alpha = .05 returns a 95% confidence interval. cols : array-like, optional cols specifies which confidence intervals to return cov_type : string The covariance type used for computing standard err

statsmodels.sandbox.distributions.transformed.Transf_gen.stats Transf_gen.stats(*args, **kwds) Some statistics of the given RV 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 (discrete RVs only) scale parameter (default=1) moments : str, optional composed of letters [?mvsk?] defining which moments

statsmodels.genmod.families.family.Binomial.fitted Binomial.fitted(lin_pred) Fitted values based on linear predictors lin_pred. Parameters: lin_pred : array Values of the linear predictor of the model. dot(X,beta) in a classical linear model. Returns: mu : array The mean response variables given by the inverse of the link function.

statsmodels.sandbox.regression.gmm.IVGMMResults.calc_cov_params IVGMMResults.calc_cov_params(moms, gradmoms, weights=None, use_weights=False, has_optimal_weights=True, weights_method='cov', wargs=()) calculate covariance of parameter estimates not all options tried out yet If weights matrix is given, then the formula use to calculate cov_params depends on whether has_optimal_weights is true. If no weights are given, then the weight matrix is calculated with the given method, and has_optimal_

statsmodels.sandbox.tsa.fftarma.ArmaFft.padarr ArmaFft.padarr(arr, maxlag, atend=True) [source] pad 1d array with zeros at end to have length maxlag function that is a method, no self used Parameters: arr : array_like, 1d array that will be padded with zeros maxlag : int length of array after padding atend : boolean If True (default), then the zeros are added to the end, otherwise to the front of the array Returns: arrp : ndarray zero-padded array Notes This is mainly written to

statsmodels.duration.hazard_regression.PHReg.fit PHReg.fit(groups=None, **args) [source] Fit a proportional hazards regression model. Parameters: groups : array-like Labels indicating groups of observations that may be dependent. If present, the standard errors account for this dependence. Does not affect fitted values. Returns a PHregResults instance. :

statsmodels.regression.linear_model.OLS.hessian OLS.hessian(params) The Hessian matrix of the model

statsmodels.duration.hazard_regression.PHRegResults.baseline_cumulative_hazard static PHRegResults.baseline_cumulative_hazard() [source] A list (corresponding to the strata) containing the baseline cumulative hazard function evaluated at the event points.

statsmodels.tsa.vector_ar.var_model.VARResults.tvalues static VARResults.tvalues() [source] Compute t-statistics. Use Student-t(T - Kp - 1) = t(df_resid) to test significance.