Hi:

Yes. You're producing eleven pairwise comparisons with a control. Look into
package multcomp and the ghlt() function, and if you have access to it, the
book Multiple Comparisons in R by Bretz, Hothorn and Westfall. Even if you
can't access the book, there are three vignettes and five demos to help you
progress in learning the multcomp package.

On Tue, Nov 23, 2010 at 5:28 PM, Steve Hong <emptican@gmail.com> wrote:

> Dear list,
>
> It may be a simple and stupid question here.  I am confused about
> correlation test and when to use Bonferroni adjustment.  I would greatly
> apprecaite if anyone can clarify it.
>

You're not performing a correlation test. Instead, you are performing
multiple pairwise comparisons with respect to the same reference category:
the control. Therefore, the tests of the eleven null hypotheses will not be
independent, so a p-value adjustment will be required. Given the nature of
the null hypotheses, you should consider looking into Dunnett's test.

I would look into other possible ways to analyze the data - more often than
not, the approach you're embarking upon is inefficient. If there are trends
or other patterns over time, it is typically better to focus on modeling the
patterns rather than performing a bunch of multiple comparisons - fewer
degrees of freedom are expended and the scientific payoff can be higher.

HTH,
Dennis

>
> I have data set consited of 12 random variables.  Say, A, B1, B2, ..., B11.
> For variable 'A', I collected only one time in early season and for
> variables B1 to B11 are repeated measurements from each plot.  My interest
> is to look at the relationship between A and each respective Bi (i=1 ~ 11).
> In that case, do I need to adjust a significant level?
>
> Thank you very much in advance!
>
> Steve
>
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