I've started using lmPerm in order to perform regressions in R. The
equation I want to fit has the form:
out3 <- lmp(outcome ~ bin1 + bin2 + cont1 + cont2, perm="Exact")
Where "outcome" is a non-normally distributed continuous variable, and bin*
and cont are binary and continuous regressors (similarly, they are
non-normally distributed). Each variable has a length of approx. 110 cases.
Here are my questions:
This code works fine, but every time it runs in R, different p-values
appear for each regressor. Which p-value should be reported in my results?
I've tried repeating the test several times (in a loop) and getting an
estimate from that, but I'm not sure it works...
If some of the predictors are changed and (then) several models /
hypothesis are tested, should Bonferroni corrections be used in the same
way they are applied for ordinary regressions? Is lmp somehow robust to
multiple testing procedures?
Thank you in advance,
[[alternative HTML version deleted]]
______________________________________________
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.
