I'm not the best statistician by a long shot, but I was trying to obtain the least squares means of my fit after fitting a mixed linear effect model using statsmodels.api.MixedLM.
By printing the summary of the fit, I only see that it returns the following table:
Mixed Linear Model Regression Results
==========================================================================
Model: MixedLM Dependent Variable: Tau
No. Observations: 155 Method: ML
No. Groups: 20 Scale: 0.0015
Min. group size: 3 Log-Likelihood: 262.8731
Max. group size: 13 Converged: Yes
Mean group size: 7.8
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Coef. Std.Err. z P>|z| [0.025 0.975]
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const 0.147 0.012 11.775 0.000 0.123 0.172
HF_or_Control (0=control, 1=HF) -0.014 0.018 -0.816 0.414 -0.049 0.020
Group Var 0.001 0.014
==========================================================================
I see that R has the function lsmeans which would actually return all the information that I'm looking for, but is there a similar function in Python's scipy or statsmodels that does something similar?