Say X is your data matrix with the variable, then you could do :
X <- matrix(rnorm(2100),300,7)
S <- var(X)
dist <- as.dist(
apply(X,1,function(i){
mahalanobis(X,i,S)
}
)
)
Cheers
Joris
On Tue, Jun 22, 2010 at 11:41 PM, yoo hoo <freesuccess2...@yahoo.com> wrote:
> I am a new R user. i have a question about Mahalanobis distance.actually i
> have 300 rows and 7 columns. columns are different measurements, 300 rows are
> genes. since genes can
> classify into 4 categories. i used dist() with euclidean distance and
> cmdscale to do MDS plot. but find out Mahalanobis distance may be
> better. how do i use Mahalanobis() to generate similar dist object which i
> can use MDS plot? second question is if should i calculate mean for
> every categories for every measurement first and do 4*4 distance matrix, or i
> should calculate pairwise distance first and then find category
> means since i only care about relative position of 4 categories in MDS
> plot. Thank you very much.
>
>
>
> [[alternative HTML version deleted]]
>
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>
--
Joris Meys
Statistical consultant
Ghent University
Faculty of Bioscience Engineering
Department of Applied mathematics, biometrics and process control
tel : +32 9 264 59 87
joris.m...@ugent.be
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