Dear R-List, I am interested in the Bayesian view on parameter estimation for multilevel models and ordinary regression models. AFAIU traditional frequentist p-values they give information about p(data_or_extreme|H0). AFAIU it further, p-values in the Fisherian sense are also no alpha/type I errors and therefor give no information about future replications.
However, p(data_or_extreme|H0) is not really interesting for social science research questions (psychology). Much more interesting is p(H0|data). Is there a way or formula to calculate these probabilities of the H0 (or another hypothesis) from lm-/lmer objects in R? Yes I know that multi-level modeling as well as regression can be done in a purely Bayesian way. However, I am not capable of Bayesian statistics, therefor I ask that question. I am starting to learn it a little bit. The frequentist literature - of course - does not cover that topic. Thanks a lot, best, leo gürtler ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
