R help - Sep 2012 - lmPerm p-values and multiple testing

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]]

Even you used perm="Exact", the maximum observations allowed is only
10.  If
data exceeds this, perm="Prob" is used instead of "Exact".
So, the p-values
are always changed.  The Porb method will approximate the permutation
distribution by randomly exchanging pairs of Y elements.



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