I have a unconventional question arising from my current master thesis on
regression modeling. Suppose we have fitted a (linear) relationship between
a dependent variable y and an independent variable x. Now we choose two
points on the x-axis, i.e. according to percentiles x10 and x90. These two
points are chosen to select the data points for two groups in order to
perform pairwise comparisons. In the first run, we choose 20 data pairs
around the x-values of x10 and x90 and run a statistical test in order to
infere if their y-values differ significantly. In a subsequent step, we
choose, say 30 data points around x90 and test against x10; in the next step
40 data points around x90 and test against x10 and so on. The intention for
this is to determine the group size and consequently the corresponding
lowest x-value where the comparison turns out to be significant.
How would you approach this problem. I thought of a many-to-one procedure
like the Dunnett test where multiple groups are tested against the same
control (which could be x10 in our example) (package multcomp or others).
However, these multiple groups would contain partly the same subjects. Or is
some sort of adaptive design the right choice. Or something completely
I'd be very happy for any kind of help since I'm completely stuck with this
question and have no idea how to solve it
View this message in context: http://r.789695.n4.nabble.com/pairwise-comparisons-in-accordance-with-regression-fit-tp4638328.html
Sent from the R help mailing list archive at Nabble.com.