3.9.3.1.1.1.2. statsmodels.regression.mixed_linear_model.MixedLM.Estep

MixedLM.Estep(fe_params, cov_re, scale)

The E-step of the EM algorithm.

This is for ML (not REML), but it seems to be good enough to use for REML starting values.

Parameters:

fe_params : 1d ndarray

The current value of the fixed effect coefficients

cov_re : 2d ndarray

The current value of the covariance matrix of random effects

scale : positive scalar

The current value of the error variance

Returns:

m1x : 1d ndarray

sum_groups \(X'*Z*E[gamma | Y]\), where X and Z are the fixed and random effects covariates, gamma is the random effects, and Y is the observed data

m1y : scalar

sum_groups \(Y'*E[gamma | Y]\)

m2 : 2d ndarray

sum_groups \(E[gamma * gamma' | Y]\)

m2xx : 2d ndarray

sum_groups \(Z'*Z * E[gamma * gamma' | Y]\)