statsmodels.iolib.table.SimpleTable.insert SimpleTable.insert(idx, row, datatype=None) [source] Return None. Insert a row into a table.

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

statsmodels.sandbox.regression.gmm.IV2SLS.score IV2SLS.score(params) Score vector of model. The gradient of logL with respect to each parameter.

statsmodels.regression.linear_model.GLSAR.fit_regularized GLSAR.fit_regularized(method='coord_descent', maxiter=1000, alpha=0.0, L1_wt=1.0, start_params=None, cnvrg_tol=1e-08, zero_tol=1e-08, **kwargs) Return a regularized fit to a linear regression model. Parameters: method : string Only the coordinate descent algorithm is implemented. maxiter : integer The maximum number of iteration cycles (an iteration cycle involves running coordinate descent on all variables). alpha : scalar or ar

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.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