statsmodels.regression.linear_model.GLS.predict GLS.predict(params, exog=None) Return linear predicted values from a design matrix. Parameters: params : array-like Parameters of a linear model exog : array-like, optional. Design / exogenous data. Model exog is used if None. Returns: An array of fitted values : Notes If the model has not yet been fit, params is not optional.

statsmodels.tools.eval_measures.bic_sigma statsmodels.tools.eval_measures.bic_sigma(sigma2, nobs, df_modelwc, islog=False) [source] Bayesian information criterion (BIC) or Schwarz criterion Parameters: sigma2 : float estimate of the residual variance or determinant of Sigma_hat in the multivariate case. If islog is true, then it is assumed that sigma is already log-ed, for example logdetSigma. nobs : int number of observations df_modelwc : int number of parameters including constant

statsmodels.tsa.kalmanf.kalmanfilter.KalmanFilter.R classmethod KalmanFilter.R(params, r, k, q, p) [source] The coefficient matrix for the state vector in the observation equation. Its dimension is r+k x 1. Parameters: r : int In the context of the ARMA model r is max(p,q+1) where p is the AR order and q is the MA order. k : int The number of exogenous variables in the ARMA model, including the constant if appropriate. q : int The MA order in an ARMA model. p : int The AR order in an

statsmodels.sandbox.distributions.transformed.ExpTransf_gen.var ExpTransf_gen.var(*args, **kwds) Variance of the distribution 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) Returns: var : float the variance of the distribution

statsmodels.sandbox.distributions.transformed.TransfTwo_gen.mean TransfTwo_gen.mean(*args, **kwds) Mean of the distribution 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) Returns: mean : float the mean of the distribution

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

statsmodels.discrete.discrete_model.LogitResults.resid_response static LogitResults.resid_response() The response residuals Notes Response residuals are defined to be where .

statsmodels.sandbox.regression.try_catdata.labelmeanfilter_nd statsmodels.sandbox.regression.try_catdata.labelmeanfilter_nd(y, x) [source]

statsmodels.regression.mixed_linear_model.MixedLM.hessian_full MixedLM.hessian_full(params) [source] Calculates the Hessian matrix for the mixed effects model with respect to the parameterization in which the covariance matrix is represented directly (without square-root transformation). Parameters: params : MixedLMParams or array-like The model parameters at which the Hessian is calculated. If array-like, must contain the packed parameters in a form that is compatible with this model inst

statsmodels.graphics.regressionplots.plot_ccpr statsmodels.graphics.regressionplots.plot_ccpr(results, exog_idx, ax=None) [source] Plot CCPR against one regressor. Generates a CCPR (component and component-plus-residual) plot. Parameters: results : result instance A regression results instance. exog_idx : int or string Exogenous, explanatory variable. If string is given, it should be the variable name that you want to use, and you can use arbitrary translations as with a formula. ax : M