[R] p(H0|data) for lm/lmer-objects R
daniel at umd.edu
Thu Dec 25 22:35:35 CET 2008
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
cuncta stricte discussurus
Von: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org] Im
Auftrag von Leo Gürtler
Gesendet: Thursday, December 25, 2008 1:52 PM
An: r-help at stat.math.ethz.ch
Betreff: [R] p(H0|data) for lm/lmer-objects R
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,
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