search for: dmahalanobis

...x <- matrix(rnorm(n*2), ncol=2) # scale, otherwise euclidean fails x <- scale(x) cr <- cov.rob(x, method="mcd") center <- cr$center # calculate squared euclidean and mahalanobis d <- rowSums(t(t(x)-center)^2) m <- as.vector(mahalanobis(x, center, cr$cov)) # euclidean an dmahalanobis basically coincide, mahalanobis slightly biased by robust covariance underestimation eqscplot(x=d, y=m); abline(0,1) # Now I add a highly redundant column in hope the distances between cases will not change x2 <- cbind(x, x[,1]+rnorm(n, sd=0.01)) # scale, otherwise euclidean fails x2 <- sca...