similar to: squared euclidean distance

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

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

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,

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

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 want to use pearson's correlation as distance between observations and then use any centroid based linkage distance (ex. Ward's distance) When linkage distances are formed as the Lance-Williams recursive formulation, they just require the initial distance between observations. See here: http://en.wikipedia.org/wiki/Ward%27s_method It is said that you have to use euclidean

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.

Hi 1. I have two raster files *.asc (identical size) 2. The data in each contain presence or absence data in each cell represented by a 1 or 0 respectively 3. I would like to take the location of each 1 (presence cell) in raster file 1 and measure the euclidean distance to the nearest 1 (presence cell) in raster file 2. Obviously in some cases there will be overlap so the distance will be zero.

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") >

?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

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

Dear all, I am trying to install Vegan, but I allways get the following error message: Warning in install.packages(choose.files("", filters = Filters[c("zip", : 'lib = "C:/Programme/R/R-2.12.0/library"' is not writable Error in install.packages(choose.files("", filters = Filters[c("zip", : unable to install packages >

Dear R-programmer, I wrote an adapted implementation of the Kennard-Stone algorithm for sample selection of multivariate data (R 2.7.1 under MacBook Pro, Processor 2.2 GHz Intel Core 2 Duo, Memory 2 GB 667 MHZ DDR2 SDRAM). I used for the heart of the script three embedded loops. This makes it especially for huge datasets very slow. For a datamatrix of 1853*1853 and the selection of 556

Hi, I'm using e1071 package to do fuzzy cluster analysis. My dataset (ra) has 5237 observations and 2 variables - depth and velocity. I used fuzzy cmeans to create 6 fuzzy classes. >ra.flcust6<-cmeans(ra,6,iter.max=100,verbose=F,dist="euclidean",method="cmea ns",m=1.7,rate.par=NULL,weights=1) I would like to calculate the value of all the fuzzy validity

> 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

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

Hello all, Sorry if this question has been answered in the past, but I could not find an answer. I am trying to print quotes within a cat output. The arguments are: file= "Data labels" directory= "/home/mylaptop/" The function returns: cat("The file", file, "is located in directory", directory, sep=" ") The output R prints is The file Data

Hola! Estoy teniendo un problema para testar la normalidad y la homogeneidad de varianzas para mi modelo de manova Es este mi modelo: m1<-manova(cbind(pred,fit,oniv)~tratamento/parcela+bloco,data.vegetacao) Aparte de tener tres variables respuestas (predador, fitófago, omnívoro), también es un diseño anidado. Alguien sabría responder esto? Gracias! Carol [[alternative HTML version