statsmodels.genmod.families.family.Family.loglike Family.loglike(endog, mu, scale=1.0) [source] The loglikelihood function. Parameters: `endog` : array Usually the endogenous response variable. `mu` : array Usually but not always the fitted mean response variable. Returns: llf : float The value of the loglikelihood evaluated at (endog,mu). Notes : ?? : This is defined for each family. endog and mu are not restricted to : `endog` and `mu` respectively. For instance, the deviance func

statsmodels.sandbox.regression.gmm.NonlinearIVGMM.gmmobjective NonlinearIVGMM.gmmobjective(params, weights) objective function for GMM minimization Parameters: params : array parameter values at which objective is evaluated weights : array weighting matrix Returns: jval : float value of objective function

statsmodels.genmod.generalized_linear_model.GLM.score GLM.score(params, scale=None) [source] score, first derivative of the loglikelihood function Parameters: params : ndarray parameter at which score is evaluated scale : None or float If scale is None, then the default scale will be calculated. Default scale is defined by self.scaletype and set in fit. If scale is not None, then it is used as a fixed scale. Returns: score : ndarray_1d The first derivative of the loglikelihood functi

statsmodels.sandbox.distributions.extras.SkewNorm_gen.entropy SkewNorm_gen.entropy(*args, **kwds) Differential entropy of the 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 Scale parameter (default=1).

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

statsmodels.tsa.arima_model.ARMAResults.initialize ARMAResults.initialize(model, params, **kwd)

statsmodels.robust.norms.TukeyBiweight class statsmodels.robust.norms.TukeyBiweight(c=4.685) [source] Tukey?s biweight function for M-estimation. Parameters: c : float, optional The tuning constant for Tukey?s Biweight. The default value is c = 4.685. Notes Tukey?s biweight is sometime?s called bisquare. Methods psi(z) The psi function for Tukey?s biweight estimator psi_deriv(z) The derivative of Tukey?s biweight psi function rho(z) The robust criterion function for Tukey?s biweight e

statsmodels.duration.hazard_regression.PHReg.weighted_covariate_averages PHReg.weighted_covariate_averages(params) [source] Returns the hazard-weighted average of covariate values for subjects who are at-risk at a particular time. Parameters: params : ndarray Parameter vector Returns: averages : list of ndarrays averages[stx][i,:] is a row vector containing the weighted average values (for all the covariates) of at-risk subjects a the i^th largest observed failure time in stratum stx,

statsmodels.discrete.discrete_model.NegativeBinomialResults.lnalpha static NegativeBinomialResults.lnalpha() [source]

statsmodels.stats.moment_helpers.cov2corr statsmodels.stats.moment_helpers.cov2corr(cov, return_std=False) [source] convert covariance matrix to correlation matrix Parameters: cov : array_like, 2d covariance matrix, see Notes Returns: corr : ndarray (subclass) correlation matrix return_std : bool If this is true then the standard deviation is also returned. By default only the correlation matrix is returned. Notes This function does not convert subclasses of ndarrays. This requires