similar to: Euclidean Distance in R

Hi there I am new to R, and I was hoping you guys could help me out. I want to make a vector that is: vec(1,2,3,4,5,6,7,8,9,10) and i want to create a function called PercentileFinder, that if i plug in PercentileFinder (vec, .9) it will give me 9. Creating the function is my biggest hurdle please e-mail me back when you all get a chance. Thank you [[alternative HTML version deleted]]

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

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]]

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.

> BDR> You will need to call it something else: dist is a clone of an S > BDR> function, and dist(X, "manhattan") is well-established usage. > > one could still imagine an extra Y argument such that > dist(X, Y=myY, method="euclidean") > and dist(X, "euclidean", Y=myY) > would work > one could even make it such that > both

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

Hi, I have a set of nodes and a dissimilarity matrix for them, as well as a csv file in which the diss matrix has been converted to [node_1, node_2, dissimilarity] format. I would like to visualize this as a graph in Euclidean space (that is, similar nodes clumped together in clusters), rather than the seriation visualization given by dissplot(). I am using Network WorkBench for my

> 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

Hello all, I've been pouring through the various spatial packages, but haven't come across the right thing yet. Given a set of points in 2-d space X, i'm trying to find the subset of points in Y proximate to each point in X. Furthermore, the proximity threshold of each point in X differs (X$threshold). I've constructed this myself already, but it's horrificly slow with a

?for doesn't return anything help.search("for") doesn't return anything- Is the for loop so prevelant in computer programing that the documentation is implicit or is R paradigm to discourage the use of the for loop. I will post data probably tonight, but here is my problem. I have preformed an MDS on a set of data. I have the scores of the four axes that are the optimal

I have a program which needs to compute squared Euclidean distance between two vectors million of times, which the Rprof shows is the bottleneck. I wondered if there is any faster way than my own simple function distance2 = function(x1, x2) { temp = x1-x2 sum(temp*temp) } I have searched the R-help archives and can not find anything except when the arguments are matrices. Thanks for any

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

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

Redbubble is developing a web app where designers, authors, illustrators, musicians and artists can upload their creative work for sale to the Australian market. Redbubble takes care of the logistics ? billing, production, marketing and distribution. We are looking for 2 developers with experience building clean, elegant and usable web applications. Toolset will be Rails, CSS, HTML, and

Hi All, I am looking for help in rotating species titles produced using the strat.plot( ) function in the rioja package. This function produces a stratigraphic plot of paleoenvironmental data. Currently the titles of each species are plotted vertically while they are typically plotted at a 45 degree angle in other programs. Does anyone have any idea of how to rotates these titles? Below is an

Hello everyone! My data is composed of 277 individuals measured on 8 binary variables (1=yes, 2=no). I did two similar cluster analyses, one on SPSS 18.0 and one on R 2.9.2. The objective is to have the means for each variable per retained cluster. 1) the R analysis ran as followed: > call data > dist=dist(data,method="euclidean") >

Dear R users I wondered if any of you ever tried to calculate distance matrix with very large data set, and if anyone out there can confirm this error message I got actually mean that my data is too large for this task. negative length vectors are not allowed My data size and code used dim(mydata_nor)[1] 365000 144> d <- dist(mydata_nor, method = "euclidean") Here my

Hi Stefan, Thank you very much for pointing me to the wordspace package. It does the job a bit faster than my C code but is 100 times more convenient. By the way, since the tcrossprod function in the Matrix package is so fast, the Euclidean distance can be computed very fast: euc_dist <- function(m) {mtm <- Matrix::tcrossprod(m); sq <- rowSums(m*m);? sqrt(outer(sq,sq,"+") -

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.