similar to: vectorization of a loop for mahalanobis distance calculation

Dear all, I've sent this question 2 days ago and got response from Sarah. Thanks for that. But unfortunately, it did not really solve our problem. The main issue is that we want to use our own (manipulated) covariance matrix in the calculation of the mahalanobis distance. Does anyone know how to vectorize the below code instead of using a loop (which slows it down)? I'd really appreciate

Hello R helpers, I'm trying to use Mahalanobis distance to calculate distance of two time series, to make some comparations with euclidean distance, DTW, etc, but I'm having some dificults. I have, for example, two objects: s.1 <- c( 5.6324702, 1.3994353, -3.2572327, -3.8311846, -1.2248719, 0.9894694, -2.2835332, -5.1969285, -5.2823988, -3.1499400, -1.7307950, 2.8221209,

How can I perform cluster analysis using the mahalanobis distance instead of the euclidean distance? Thank you Maria Froes [[alternative HTML version deleted]]

http://www.nabble.com/file/p24569511/mahalanobis.txt mahalanobis.txt http://www.nabble.com/file/p24569511/concentrations.txt concentrations.txt Dear Forum members, I have a problem calculating mahalanobis distances. My data file mahalanobis.txt and categories file concentrations.txt are attached. I do the following steps: x <- as.matrix(read.table("mahalanobis.txt", header=TRUE))

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

Dear R ExpeRts, I'm having memory difficulties using mahalanobis distance to trying to cluster in R. I was wondering if anyone has done it with a matrix of 6525x17 (or something similar to that size). I have a matrix of 6525 genes and 17 samples. I have my R memory increased to the max and am still getting "cannot allocate vector of size" errors. My matrix "x" is

I want to calculate the probability that a group will include a particular point using the squared Mahalanobis distance to the centroid. I understand that the squared Mahalanobis distance is distributed as chi-squared but that for a small number of random samples from a multivariate normal population the Hotellings T2 (T squared) distribution should be used. I cannot find a function for

How can I perform cluster analysis using the mahalanobis distance instead of the euclidean distance? thank you Naama Wolf -- View this message in context: http://n4.nabble.com/cluster-with-mahalanobis-distance-tp1577038p1577038.html Sent from the R help mailing list archive at Nabble.com.

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

Is there a function that calculate the mahalanobis distance in R . The dist function calculates "euclidean"', '"maximum"', '"manhattan"', '"canberra"', '"binary"' or '"minkowski"'. Thanks ../Murli

Hi I have the following problem: given x (px1) and S (pXp positive definite), find y such that y_i<=0 (i=1..p) minimizing the mahalanobis distance (x-y)'S^{-1}(x-y). Has anyone worked on this problem? Tips or R code would be appreciated. David ____________________ David Edwards Principal scientist Biostatistics Novo Nordisk A/S Novo Allé 2880 Bagsvaerd Denmark +45 4444 8888 (phone)

Dear R-experts, Searching the help archives I found a recommendation to do multivariate outlier identification by mahalanobis distances based on a robustly estimated covariance matrix and compare the resulting distances to a chi^2-distribution with p (number of your variables) degrees of freedom. I understand that compared to euclidean distances this has the advantage of being scale-invariant.

Dear all Why isn'it possible to calculate Mahalanobis distances with R for a matrix with 1 row (observations) more than the number of columns (variables)? > mydata <- matrix(runif(12,-5,5), 4, 3) > mahalanobis(x=mydata, center=apply(mydata,2,mean), cov=var(mydata)) [1] 2.25 2.25 2.25 2.25 > mydata <- matrix(runif(420,-5,5), 21, 20) > mahalanobis(x=mydata,

Hi, help on 'mahalanobis' (in the stats package in Rv2.2.0) now says: "Description: Returns the Mahalanobis distance of all rows in 'x' and the vector mu='center' with respect to Sigma='cov'. This is (for vector 'x') defined as D^2 = (x - mu)' Sigma^{-1} (x - mu)" It does return D^2 as written. However,

Hello all, I am a 2 month newbie to R and am stumped. I have a data set that I've run multivariate stats on using the manova function (I included the data set). Now it comes time for a table of effect sizes with significance. The univariate tests are easy. Where I run into trouble filling in the table of effect sizes is the Mahalanobis D as an effect size. I've included the table so

Hello all, I am a 2 month newbie to R and am stumped. I have a data set that I've run multivariate stats on using the manova function (I included the data set). Now it comes time for a table of effect sizes with significance. The univariate tests are easy. Where I run into trouble filling in the table of effect sizes is the Mahalanobis D as an effect size. I've included the table so

The help on mahalanobis {stats} does not include any reference. I'm interested in understand why Mahalanobis is defined in its current way and how to use it. Could somebody point me a good book on this? I have looked through a few books, but they all give very light explanation on it.

Dear R community Have just recently got back into R after a long break and have been amazed at how much it has grown, and how active the list is! Thank you so much to all those who contribute to this amazing project. My question: I am trying to calculate Mahalanobis distances for a matrix called "fgmatrix" >dim(fgmatrix) [1] 76 15 >fg.cov <- cov.wt(fgmatrix)

Hi all. I am seeking for a statistical suggestion. My data set comprises 382 measures each having 169 variables. Each measure is the outcome of a nuclear magnetic resonance experiment, so each of the 169 points has the same unit. As I want to do some multivariate calibration using these data, I checked whether some multivariate outliers existed. I calculated Mahalanobis distances and did a

Dear R list, I'm trying to calculate Mahalanobis distances for 'Species' of 'iris' data as obtained below: Squared Distance to Species From Species: Setosa Versicolor Virginica Setosa 0 89.86419 179.38471 Versicolor 89.86419 0 17.20107 Virginica 179.38471 17.20107 0 This distances above were obtained with proc