g-ecology-boun...@r-project.org] On Behalf Of Chris Howden
> Sent: Tuesday, March 05, 2013 10:45 PM
> To: 张勇; r-sig-ecology@r-project.org
> Subject: Re: [R-sig-eco] Should one remove highly correlated variables before
> doing PCA??
>
> Hi Yong,
>
> PCA is a way to deal with
Tuesday, March 05, 2013 10:45 PM
To: 张勇; r-sig-ecology@r-project.org
Subject: Re: [R-sig-eco] Should one remove highly correlated variables before
doing PCA??
Hi Yong,
PCA is a way to deal with highly correlated variables, so there is no need to
remove them.
If N variables are highly corre
ssage-
From: r-sig-ecology-boun...@r-project.org
[mailto:r-sig-ecology-boun...@r-project.org] On Behalf Of ??
Sent: Wednesday, 6 March 2013 4:33 PM
To: r-sig-ecology@r-project.org
Subject: [R-sig-eco] Should one remove highly correlated variables before
doing PCA??
Hi list,
Maybe this is not a "R
Hi list,
Maybe this is not a "R" question, however, it has bothered me for a long time.
Some people think if a set of correlated variables might "load" onto several
principal components (eigenvectors),so including many variables from such a set
will differentially weight several eigenvectors--
