This is very opaque to me. But if H0 is a null hypothesis (i.e. a hypothesis about one or several coefficients in your model), then you can test linear or nonlinear restrictions of the coefficients. Because your coefficients are derived using your data, it appears to me you get something like a p(H0|data).
------------------------- cuncta stricte discussurus ------------------------- -----Ursprüngliche Nachricht----- Von: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] Im Auftrag von Leo Gürtler Gesendet: Thursday, December 25, 2008 1:52 PM An: r-h...@stat.math.ethz.ch Betreff: [R] p(H0|data) for lm/lmer-objects R 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. ______________________________________________ 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.
