statsmodels.genmod.families.family.NegativeBinomial class statsmodels.genmod.families.family.NegativeBinomial(link=, alpha=1.0) [source] Negative Binomial exponential family. Parameters: link : a link instance, optional The default link for the negative binomial family is the log link. Available links are log, cloglog, identity, nbinom and power. See statsmodels.family.links for more information. alpha : float, optional The ancillary parameter for the negative binomial distribution. For

statsmodels.robust.robust_linear_model.RLMResults.weights static RLMResults.weights() [source]

Linear Mixed Effects Models Link to Notebook GitHub In [1]: import numpy as np import statsmodels.api as sm import statsmodels.formula.api as smf In [2]: %load_ext rpy2.ipython In [3]: %R library(lme4) Loading required package: Matrix Loading required package: Rcpp Attaching package: ?lme4? The following object is masked from ?package:robustbase?: sigma Comparing R lmer to Statsmodels MixedLM The Statsmodels imputation of linear mixed mod

statsmodels.discrete.discrete_model.NegativeBinomialResults.f_test NegativeBinomialResults.f_test(r_matrix, cov_p=None, scale=1.0, invcov=None) Compute the F-test for a joint linear hypothesis. This is a special case of wald_test that always uses the F distribution. Parameters: r_matrix : array-like, str, or tuple array : An r x k array where r is the number of restrictions to test and k is the number of regressors. It is assumed that the linear combination is equal to zero. str : The full

statsmodels.discrete.discrete_model.Logit.information Logit.information(params) Fisher information matrix of model Returns -Hessian of loglike evaluated at params.

statsmodels.sandbox.sysreg.SUR.initialize SUR.initialize() [source]

statsmodels.discrete.discrete_model.MultinomialModel.fit_regularized MultinomialModel.fit_regularized(start_params=None, method='l1', maxiter='defined_by_method', full_output=1, disp=1, callback=None, alpha=0, trim_mode='auto', auto_trim_tol=0.01, size_trim_tol=0.0001, qc_tol=0.03, **kwargs) [source] Fit the model using a regularized maximum likelihood. The regularization method AND the solver used is determined by the argument method. Parameters: start_params : array-like, optional Initia

statsmodels.discrete.discrete_model.Poisson.cov_params_func_l1 Poisson.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.genmod.families.links.Link.deriv2 Link.deriv2(p) [source] Second derivative of the link function g??(p) implemented through numerical differentiation

statsmodels.tsa.arima_model.ARMAResults.fittedvalues static ARMAResults.fittedvalues() [source]