Hello,
I am a new R user and trying to learn how to implement the mahalanobis
function to measure the distance between to 2 population centroids.  I
have used STATISTICA to calculate these differences, but was hoping to learn
to do the analysis in R.  I have implemented the code as below, but my
results are very different from that of STATISTICA, and I believe I may not
have interpreted the help correctly and may have implemented the
code incorrectly.

Though I am not certain, I believe that my error may be in calculating the
common covariance matrix (the third argument supplied to the mahalanobis
funtion).

Any help or guidance would be greatly appreciated.

Thank you! RL

CODE

fit<-lda(pop~v1 + v2 + v3 +...+vn, data=my.data)

x1<-subset(my.data, pop==1)

x2<-subset(my.data, pop==2)



 #Save Covariance Matices for each group
cov1<-cov(x1)
cov2<-cov(x2)



#Determine number of rows in each matrix
n1<-nrow(x1); n2<-nrow(x2);
n.rows<-c(n1,n2)


#store mean vectors from lda object
mu1<-fit$means[1,]
mu2<-fit$means[2,]



#Calculate the common Covariance Matrix
S<-(((n.rows[1]-1)*cov1)+((n.rows[2]-1)*cov2)/ (sum(n.rows[1:2])-1))

#Calculate the common Covariance Matrix
mahalanobis(mu1, mu2, S)

	[[alternative HTML version deleted]]

