Re: [R] linear models and colinear variables...

From: Peter Gaffney <petertgaffney_at_yahoo.com>
Date: Fri 02 Jul 2004 - 10:53:43 EST


Hi!

> When you do this, you are including all the
> interaction terms.
> The * indicates an interaction, as opposed to +.

In this particular case I need to do exactly this; this is a study of antibiotic resistance - two of the variables respectively are type of bacteria and antibacterial agent. The evolutionary/epidemiological behavior of each pairing of these factors is different. Can I remove some lower order terms; for example, if I get rid of Bugtype:Usage.level.ofdrug and Drugtype:Usage.level.of.drug will
Bugtype:Drugtype:Usage.level.of.drug still be valid?

> If you select predictors on the basis of which ones
> are
> significant, then the final significance levels
> don't mean much,
> usually. Remember, 1 out of 20 will be significant
> at .05 even
> if you are using random numbers.
>

This is an excellent point; were I to proceed I would need to select based strictly on removing from collinear pairs or groups of explanatory variables, probably according to an a priori established ordering of classes of variables; ie B:D:U might be more interesting than B:U or D:U or B:D:U:ICU, so remove collinear variables from the latter three first, irrespective of statistical significance.

Thanks for you help. :-)

-petertgaffney



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