Hello,

I'm planning on using a regression model to describe seed set of plants (my
response) using some sort of predictor based on temperature.  I have a
number of temperature variables calculated from the same set of data
(hourly temperatures for the growing season, converted to variables such as
average temperature, maximum temperature, minimum temperature, degree-days
above zero Celsius, degree days above ten Celsius, etc...), and I want to
decide which one should be included in my model. I know that I would
ideally select one based on "prior knowledge" of the system (e.g. so-called
"planned comparisons" or choosing a temperature threshold that is known to
be important for the development of seeds), but not much is known about
this system.

I've been warned against testing the significance of multiple predictors
using p-values, unless I use Bonferroni correction (or some equivalent).
Unfortunately, using Bonferroni correction would result in something like p
= 0.05/7 (for seven different temperature variables); a rather small value
for detecting anything! I was wondering whether it would be appropriate to
instead use likelihood-based techniques (direct comparisons of
log-likelihoods or AIC scores) to compare a series of models using each of
the alternative predictors in turn, and choose the most relevant
temperature variable (i.e. predictor) based on that.

Thoughts on the validity of this approach? Would any adjustments have to be
made for multiple comparisons if I used this strategy?

Jason Straka
University of Victoria

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