As we know, Inference from GLMMs is complicated. Except for cases where there are many observations at each level (particularly the highest), assuming that (frac{Estimate}{SE}) is normally distributed may not be accurate. A variety of alternatives have been suggested including Monte Carlo simulation, Bayesian estimation, and bootstrapping for simulation purpose. Each of these can be complex to implement.
Therefore, how to infer or generalize results from GLM in simple way?
Stephen Politzer-Ahles
, Testing statistical significance of coefficients is a little part which is easier to see Score value. My question was just focusing on simulation from the whole result. Since there is both fixed and random effect, how to make sure that the outputs are significant in prediction probabilities?For more, please, see the attached below.
https://stats.idre.ucla.edu/r/dae/mixed-effects-logistic-regression/
Thank you for your participation! I really appreciate it. All the points you mentioned are easiest part of interpretation for each supposed variables.
Can you talk more about how predictions probability behave.
If you have time,please, see the attached document section in multilevel bootstrap part.
Here is an earlier RG thread on reporting multilevel model etsimates: https://www.researchgate.net/post/How_do_I_report_the_results_of_a_linear_mixed_models_analysis#view=5cb8eb822ba3a1c4aa35926e
You may also find my online book helpful:
https://www.researchgate.net/project/Book-New-statistics-for-the-design-researcher
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