similar to: dist(*, "euclidean") [was "dist function suggestion"]

the first line of dist() says if (!is.na(pmatch(method, "euclidian"))) shouldn't that be "euclidean" ? --------------------- R version 2.5.1 (2007-06-27) i486-pc-linux-gnu locale:

Dear Users,I have two matrices A=15*365 and B=1*365. i want to calculate "Euclidean Distance" between these matrices in such a way that i should have euclidean distance of matrix B against all the columns of matrix A. More precisely, first i want euclidean dist. of column 1 of A against B, then column 2 against B, 3rd column of A against B and so on.is there a way in r to do it?your help

Vince Carey found this (thank you!). Since the fix to the problem is not entirely obvious, I post this to R-devel as RFC: help(dist) says: >> Missing values are allowed, and are excluded from all computations >> involving the rows within which they occur. If some columns are >> excluded in calculating a Euclidean, Manhattan or Canberra >> distance, the sum is

Hello, I am working with a matrix of multilocus genotypes for ~180 individual snail samples, with substantial missing data. I am trying to calculate the pairwise genetic distance between individuals using the stats package 'dist' function, using euclidean distance. I took a subset of this dataset (3 samples x 3 loci) to test how euclidean distance is calculated: 3x3 subset used

> 3x3 subset used > Locus1 Locus2 Locus3 > Samp1 GG <NA> GG > Samp2 AG CA GA > Samp3 AG CA GG > > The euclidean distance function is defined as: sqrt(sum((x_i - y_i)^2)) My > assumption was that the difference between

Hi, I'm plotting 4 graphs on one page (2x2 matrix) but I cant seem to get the title for the whole page right. I'm doing: op <- par(mfrow = c(2,2), pty="s") hist(var$V2, breaks="FD",main="Euclidean Metric", xlab="Sum of 3NN ... hist(var$V2, breaks="FD",main="Manhattan Metric", xlab="Sum of 3NN ... hist(var$V2,

This message is in MIME format. The first part should be readable text, while the remaining parts are likely unreadable without MIME-aware tools. Send mail to mime@docserver.cac.washington.edu for more info. ---559023410-162216788-916833047=:29339 Content-Type: TEXT/PLAIN; charset=US-ASCII On my R installation (0.62.4) there is no dist() function, so I attach one possibility. It provides

Dear R users, When I run the following code, R crashes: require(cclust) x <- matrix(c(0,0,0,1.5,1,-1), ncol=2, byrow=TRUE) cclust(x, centers=x[2:3,], dist="manhattan", method="kmeans") While this works: cclust(x, centers=x[2:3,], dist="euclidean", method="kmeans") I'm posting this here because I am not sure if it is a bug. I've been searching

Sorry about the odd terminology, but I suspect that my intent might be completely missed had I used "aggregate" or "classify" (each of which appears to have some rather special meanings in statistical analysis and modeling). I have some data about software builds; one of the characteristics of each is the name of the branch. A colleague has generated some fairly interesting

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

Hello all! I am performingsome clustering analysis on microarray data using agnes{cluster} and I have created my own dissimilarity matrix according to a distance measure different from "euclidean" or "manhattan" etc. My question is, if I choose for example method="complete", how are the distances between the elements calculated? Are they taken form the dissimilarity

Hi, I would like to calculate the RELATIVE euclidean distance. Is there a function in R which does it ? (I calculated the abundance of 94 chemical compounds in secretion of several individuals, and I would like to have the chemical distance between 2 individuals as expressed by the relative euclidean distance. Some compounds are in very low abundance whereas others are in high abundance,

I am studying on statistical shape analysis, I wonder is there any way or package available that I can perform Euclidean Distance Matrix Analysis (EDMA I or EDMA II) in R... thanks Gokhan -- View this message in context: http://r.789695.n4.nabble.com/Euclidean-Distance-Matrix-Analysis-EDMA-in-R-tp2266797p2266797.html Sent from the R help mailing list archive at Nabble.com.

I use the following code to scan a (limited) parameter space of clustering strategies ... data <- read.table(... dataTranspose <- t(data) distMeth <- c("euclidean", "maximum", "manhattan", "canberra", "binary" ) clustMeth <- c("ward",

Hello, I analyse some free-sorting data so I use hierarchical clustering. I want to compare my proximity matrix with the tree representation to evalute the fitting. (stress, cophenetic correlation (pearson's correlation)...) "The cophenetic similarity of two objects a and b is defined as the similarity level at wich objects a and b become members of the same cluster during the course of

Hi, I should preface this problem with a statement that although I am sure this is a really easy function to write, I have tried and failed to get my head around writing functions in R. I can use R where functions exist to do what I want done, but have found myself completely incapable of writing them myself. The problem is that I have a table with several rows of species and several columns of

***********reading in data********** data<-read.table("microarray.txt",header=T, sep="\t") head(data) dim(data) attach(data) ***********creating matrix and calculating variance across probesets******** x<-1:20000 y<-2:141 data.matrix<-data.matrix(data[,y]) variableprobe<-apply(data.matrix[x,],1,var) hist(variableprobe) **************filter out low

> By the way, since the tcrossprod function in the Matrix package is so fast, the Euclidean distance can be computed very fast: Indeed. > euc_dist <- function(m) {mtm <- Matrix::tcrossprod(m); sq <- rowSums(m*m); sqrt(outer(sq,sq,"+") - 2*mtm)} There are two reasons why I didn't use this optimization in "wordspace": 1) It can be inaccurate for small

Dear R-Help-Team, I would like to cluster my data using the ward-method. In several papers I read (e.g. Bahrenberg) that it is neccesary to use the "squared euclidean distance" with the ward-method. Unfortunatelly I cannot find this term in r as a method for measuring the distance. Does anybody have an idea? Thanks in advance, Carolin [[alternative HTML version deleted]]

a <- c(1:10) b <- c(.5, .6, .9, 10, .4, 3, 4, 9, 0, 11) d <- c(21:30) z <- data.frame(a,b,d) library(fields) results <- c() for(i in 1:(length(rownames(z))-1)){ results[i] <- rdist(z[i,], z[(i+1),]) } results.1 <- data.frame(results) f <- rownames(z) r <- f[-1] rownames(results.1) <- r colnames(results.1) <- f[1] this does what I want it to do - is