MixedLM.Estep(fe_params, cov_re, scale)[source]

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.


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


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]

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