statsmodels.iolib.table.SimpleTable.insert_stubs SimpleTable.insert_stubs(loc, stubs) [source] Return None. Insert column of stubs at column loc. If there is a header row, it gets an empty cell. So len(stubs) should equal the number of non-header rows.

statsmodels.tsa.vector_ar.var_model.FEVD.cov FEVD.cov() [source] Compute asymptotic standard errors

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

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

statsmodels.genmod.generalized_estimating_equations.GEE.estimate_scale GEE.estimate_scale() [source] Returns an estimate of the scale parameter phi at the current parameter value.

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

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

statsmodels.sandbox.distributions.extras.ACSkewT_gen.est_loc_scale ACSkewT_gen.est_loc_scale(*args, **kwds) est_loc_scale is deprecated! This function is deprecated, use self.fit_loc_scale(data) instead.

statsmodels.tsa.ar_model.AR.fit AR.fit(maxlag=None, method='cmle', ic=None, trend='c', transparams=True, start_params=None, solver='lbfgs', maxiter=35, full_output=1, disp=1, callback=None, **kwargs) [source] Fit the unconditional maximum likelihood of an AR(p) process. Parameters: maxlag : int If ic is None, then maxlag is the lag length used in fit. If ic is specified then maxlag is the highest lag order used to select the correct lag order. If maxlag is None, the default is round(12*(no