similar to: About help on 'mahalanobis'

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.

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,

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

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

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.

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

Dear listers: I have an idea to do the outlier detection and I need to use R to implement it first. Here I hope I can get some input from all the guru's here. I select distance-based approach--- step 1: calculate the distance of any two rows for a dataframe. considering the scaling among different variables, I choose mahalanobis, using variance as scaler. step 2: Let k be the number of

Dear All, I am not able to run the random forest with my dataset.. X<- 280 records with satellite data(28 columns) - B1min, b1max, b1std etc.. y<- 280 records with 3 columns - TotBasal Area, Stem density and Volume yref <- y[1:230,] #Keeping 1st 230 records as reference records want to set 0 to y values for records 231 to 280.. yimp <- y[231:280,] #records for which we want

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 These distances were obtained with proc 'CANDISC'

Hello. Is it possible to choose the distance in the kmeans algorithm? I have m vectors of n components and I want to cluster them using kmeans algorithm but I want to use the Mahalanobis distance or another distance. How can I do it in R? If I use kmeans, I have no option to choose the distance. Thanks in advance, Arnau.

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))

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,

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

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

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

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

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

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)

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 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