[R-lang] Collinearity and condition number

[T. Florian Jaeger]

Hi Claire,

More generally, too, it is not surprising to find that two codings differ in
collinearity (e.g. treatment coding vs. contrast/sum coding). Which coding
you should use depends on (a) what hypothesis you want to test and (b) if
you need to reduced collinearity (b/c you need reliable estimates of effect
direction and/or effect shape, since otherwise you could simply use model
comparison).

florian

On Mon, Apr 20, 2009 at 6:16 AM, Matthew Roberts
<Matthew.Roberts at ed.ac.uk>wrote:

> Are you sure that the numeric values are the same in each case? When R
> converts factor() to numeric(), it take the numerical factor id rather
> than the name. To avoid this do:
>
> numeric.condition.number <-
> as.numeric(as.character(factor.condition.number))
>
> Hope this helps,
>
> Matthew
>
> * Claire Delle Luche <Claire.Delleluche at univ-lyon2.fr> [2009-04-20
> 10:41:58 +0200]:
>
> > Dear R-users,
> >
> > I am running a mixed effect model on a written corpus.
> > When I check for collinearity, I get a value of 35 for condition number
> when my predictors are entered as names then transformed as numeric (the
> values are 1 and 2 for two level predictors after the transormation).
> > However, when I enter the predictors as factors and assign levels of 0
> and 1 instead of names (and convert them as numeric), I get a condition
> number of 12.
> >
> > For the same data, depending on how I code the predictors, I either have
> moderate or important collinearity. What shall I do?
> > Which coding is more acceptable?
> >
> > Thanks very much in advance.
> >
> > Yours,
> >
> > Claire Delle Luche
> > Laboratoire Dynamique du Langage
> > 14, avenue Berthelot
> > 69 007 Lyon
> > France
> >
> > _______________________________________________
> > R-lang mailing list
> > R-lang at ling.ucsd.edu
> > http://pidgin.ucsd.edu/mailman/listinfo/r-lang
> >
>
> --
> Matthew A. J. Roberts
> Department of Psychology,
> University of Edinburgh,
> 7 George Square
> EH8 9JZ
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