This question already has an answer here:
- When no model comparison, should I use REML vs ML? 2 answers
- Why does one have to use REML (instead of ML) for choosing among nested var-covar models? 3 answers
- Why are the coefficients of REML and ML estimation the same? What does that mean? 1 answer
- Citation for ML vs. REML 1 answer
- Likelihood ratio tests using ML vs. REML 1 answer
I'm trying to fit linear mixed models to 3 different DV (so three models). I understand that REML gives less biased variance estimates. As im more interested in the fixed effects, I use ML for the initial stepwise model reduction based on AIC-values, and use REML to fit my final (reduced) models.
However, if I got that right, REML ignores the fixed part for fitting the model, right? And since 2 of my 3 models have only very little random variance, I'm confused whether I should completely stick to ML-estimates? What is your opinion on this? Am I right in my understanding of REML vs. ML?