It looks like SR, SU and ST are strongly correlated to each other, as well as
DR, DU and DT.
You can try to do PCA on your 6 variables, pick the first 2 principal
components as your new variables and use them for regression.
--- On Fri, 11/7/08, Georg Ehret <[EMAIL PROTECTED]> wrote:
> From: Georg Ehret <[EMAIL PROTECTED]>
> Subject: [R] number of effective tests
> To: "r-help" <[EMAIL PROTECTED]>
> Received: Friday, 11 July, 2008, 11:46 AM
> Dear R community,
> I am using 6 variables to test for an effect (by
> linear regression).
> These 6 variables are strongly correlated among each other
> and I would like
> to find out the number of independent test that I perform
> in this
> calcuation. For this I calculated a matrix of correlation
> coefficients
> between the variables (see below). But to find the rank of
> the table in R is
> not the right approach... What else could I do to find the
> effective number
> of independent tests?
> Any suggestion would be very welcome!
> Thanking you and with my best regards, Georg.
>
> > for (a in 1:6){
> + for (b in 1:6){
> +
> r[a,b]<-summary(lm(unlist(d[a])~unlist(d[b])),na.action="na.exclude")$adj.r.squared
> + }
> + }
> >
> > r
> SR SU ST DR DU
> DT
> SR 1.0000000 0.9636642 0.9554952 0.2975892 0.3211303
> 0.3314694
> SU 0.9636642 1.0000000 0.9101678 0.3324979 0.3331389
> 0.3323826
> ST 0.9554952 0.9101678 1.0000000 0.2756876 0.3031676
> 0.3501157
> DR 0.2975892 0.3324979 0.2756876 1.0000000 0.9981733
> 0.9674843
> DU 0.3211303 0.3331389 0.3031676 0.9981733 1.0000000
> 0.9977780
> DT 0.3314694 0.3323826 0.3501157 0.9674843 0.9977780
> 1.0000000
>
> *************************
> Georg Ehret
> Johns Hopkins University
> Baltimore, US
>
> [[alternative HTML version deleted]]
>
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