similar to: exploring dist()

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

Hello everybody. I am trying to cluster circular data (data points which are angles), thus i can not use the "dist" function in "mclust" to generate my distance matrix, I am using the function " Dij = 0.5*( 1 - cos(theta_i - theta_j)). The thing is "hclust" will not accept this distance matrix, i tried to put it in a data frame, but again i get an error message

I've been unable to find a R package that provides the means of performing Clarke & Ainsworth's BIO-ENV procedure or something comparable. Briefly, they describe a method for comparing two separate sample ordinations, one from species data and the second from environmental data. The analysis includes selection of the 'best' subset of environmental variables for explaining

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

Hello I am using Jari Oksanen's CCA routine from the Vegan package on some estuary data, following a technique applied in (Anderson, M.J. & Gribble, N.A., 1998, Partitioning the variation among spatial, temporal and environmental components in a multivariate data set, Australian Journal of Ecology 23, 158-167). Some steps in the process require that the dependent matrix be constrained by

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,"+") -

Hello: What I want to do is quite simple, but I can't find a way. I have a data frame with several points (x and y coords). I want to add another column with cluster membership. For example aggregate all the points that stand within a distance of 40 from each other. I've tried using "nncluster" from the package nnclust, but the results are not correct, for some

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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:

I work with Windows and use R version 2.4.1. I am JUST starting to learn this program... I get this warning message 'NAs introduced by coercion' while trying to build a distance matrix (to be analyzed with NMDS later) from a 336 x 100 data matrix. The original matrix has lots of zeros and no missing values, but I don't think this should matter. I searched this forum and people have

Dear List, Apologies for this off-topic post but it is R-related in the sense that I am trying to understand what R is telling me with the data to hand. ROC curves have recently been used to determine a dissimilarity threshold for identifying whether two samples are from the same "type" or not. Given the bashing that ROC curves get whenever anyone asks about them on this list (and

How could one get started with a self-written function? I have a function written in .txt format, but can not find the way to import it to the R space. Would be very appreciated for help. -- View this message in context: http://r.789695.n4.nabble.com/run-a-slef-written-function-tp3356537p3356537.html Sent from the R help mailing list archive at Nabble.com.

Hello, I am trying to do a multi-dimensional scaling of the World Bank's quality of governance indicators for the Balkan region. I am having trouble labelling my plot. Could some kind person help me out. How do I set the attribute Label by a variable (say, "Code")? At present I get this: >qog.dist<-dist(Balkans.data, method = "euclidean", diag = FALSE, upper =

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

Hi, I am currently working on some data and feel that NMDS would return an excellent result. With my current data set however I have been experiencing some problems and cannot carry out metaMDS. I have tried with a few smaller data sets which I created for practice sake and this has worked fine. I think it is the set up of my data set that is causing me trouble. I have 18 columns and 18 rows,

> 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 guys, I've been using the kmeans and hclust functions for some time now and was wondering if I could specify a custom metric when passing my data frame into hclust as a distance matrix. Actually, kmeans doesn't even take a distance matrix; it takes the data frame directly. I was wondering if there's a way or if there's a package that lets you create distance matrices from

i'm working on some distance matrices and i was wondering if there is a way to export the matrices from R to excel. OG thanks [[alternative HTML version deleted]]

H ello R-experts, I want to do ordination plots using vegan metaMDS. I have a where many cells have zero values. Data structure: X[1:10,1:14] Height.1 Height.2 Height.3 Height.4 Height.5 Height.6 Height.7 Height.8 Height.9 Height.10 Height.11 Height.12 Height.13 D30I1A 46 0 0 0 0 0 0 0 0 0 39 0 98 D30I1B

Hi, I have a data frame with column names "1", "2", "3", ... and I'd like to extract a subset based on the values in the first column. None of the methods I tried worked (below). x <- subset(dframe, 1 = = "My Text") x <- subset(dframe, "1" = = "My Text") x <- subset(dframe, names(dframe)[1] = = "My Text") Q