statsmodels.regression.linear_model.OLSResults.cov_params OLSResults.cov_params(r_matrix=None, column=None, scale=None, cov_p=None, other=None) Returns the variance/covariance matrix. The variance/covariance matrix can be of a linear contrast of the estimates of params or all params multiplied by scale which will usually be an estimate of sigma^2. Scale is assumed to be a scalar. Parameters: r_matrix : array-like Can be 1d, or 2d. Can be used alone or with other. column : array-like, opti

statsmodels.regression.linear_model.OLSResults.rsquared_adj static OLSResults.rsquared_adj()

statsmodels.sandbox.regression.gmm.LinearIVGMM.predict LinearIVGMM.predict(params, exog=None) [source]

statsmodels.stats.weightstats.DescrStatsW.sum_weights static DescrStatsW.sum_weights() [source]

statsmodels.sandbox.regression.gmm.LinearIVGMM.momcond_mean LinearIVGMM.momcond_mean(params) mean of moment conditions,

statsmodels.tsa.arima_model.ARIMA.loglike_css ARIMA.loglike_css(params, set_sigma2=True) Conditional Sum of Squares likelihood function.

statsmodels.sandbox.regression.gmm.IVRegressionResults.uncentered_tss static IVRegressionResults.uncentered_tss()

statsmodels.iolib.table.SimpleTable.append SimpleTable.append() L.append(object) ? append object to end

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