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
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
lmpsomehow robust to multiple testing procedures?