statsmodels.miscmodels.count.PoissonGMLE class statsmodels.miscmodels.count.PoissonGMLE(endog, exog=None, loglike=None, score=None, hessian=None, missing='none', extra_params_names=None, **kwds) [source] Maximum Likelihood Estimation of Poisson Model This is an example for generic MLE which has the same statistical model as discretemod.Poisson. Except for defining the negative log-likelihood method, all methods and results are generic. Gradients and Hessian and all resulting statistics are b

statsmodels.genmod.families.links.Link class statsmodels.genmod.families.links.Link [source] A generic link function for one-parameter exponential family. Link does nothing, but lays out the methods expected of any subclass. Methods deriv(p) Derivative of the link function g?(p). deriv2(p) Second derivative of the link function g??(p) inverse(z) Inverse of the link function. inverse_deriv(z) Derivative of the inverse link function g^(-1)(z).

statsmodels.discrete.discrete_model.MultinomialModel.cov_params_func_l1 MultinomialModel.cov_params_func_l1(likelihood_model, xopt, retvals) Computes cov_params on a reduced parameter space corresponding to the nonzero parameters resulting from the l1 regularized fit. Returns a full cov_params matrix, with entries corresponding to zero?d values set to np.nan.

statsmodels.regression.linear_model.OLSResults.get_influence OLSResults.get_influence() [source] get an instance of Influence with influence and outlier measures Returns: infl : Influence instance the instance has methods to calculate the main influence and outlier measures for the OLS regression See also statsmodels.stats.outliers_influence.OLSInfluence

statsmodels.sandbox.regression.gmm.GMM.momcond_mean GMM.momcond_mean(params) [source] mean of moment conditions,

statsmodels.sandbox.regression.gmm.IVGMM.gradient_momcond IVGMM.gradient_momcond(params, epsilon=0.0001, centered=True) gradient of moment conditions Parameters: params : ndarray parameter at which the moment conditions are evaluated epsilon : float stepsize for finite difference calculation centered : bool This refers to the finite difference calculation. If centered is true, then the centered finite difference calculation is used. Otherwise the one-sided forward differences are used.

statsmodels.stats.inter_rater.aggregate_raters statsmodels.stats.inter_rater.aggregate_raters(data, n_cat=None) [source] convert raw data with shape (subject, rater) to (subject, cat_counts) brings data into correct format for fleiss_kappa bincount will raise exception if data cannot be converted to integer. Parameters: data : array_like, 2-Dim data containing category assignment with subjects in rows and raters in columns. n_cat : None or int If None, then the data is converted to integ

statsmodels.stats.outliers_influence.OLSInfluence.params_not_obsi static OLSInfluence.params_not_obsi() [source] (cached attribute) parameter estimates for all LOOO regressions uses results from leave-one-observation-out loop

statsmodels.genmod.families.family.NegativeBinomial.fitted NegativeBinomial.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.regression.mixed_linear_model.MixedLM.score MixedLM.score(params) [source] Returns the score vector of the profile log-likelihood. Notes The score vector that is returned is computed with respect to the parameterization defined by this model instance?s use_sqrt attribute. The input value params can be with respect to any parameterization.