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Fig. 2 | BMC Medical Research Methodology

Fig. 2

From: High performance implementation of the hierarchical likelihood for generalized linear mixed models: an application to estimate the potassium reference range in massive electronic health records datasets

Fig. 2

Performance of GLMM fitting methods in the less variable Binomial datasets: fixed effects (A), variance components (B) and random effects (C). For set of parameters we show the standardized Bias (stdBias) and the Mean Square Error (MSE). Abbreviations: AGH0(1): Adaptive Gaussian Quadrature of order 0 or 1, glmmTMB: estimates returned by the relevant package in R, h-lik: the proposed method in text, h-lik-pub: estimates returned by the first optimization for pu, β(h) in the proposed implementation, h-lik-3, random effect estimates from the optimization of \( h\left(\beta, u,\hat{\gamma},\hat{\phi};Y,u\right) \) over the fixed and random effects

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BMC Medical Research Methodology

ISSN: 1471-2288

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