similar to: cluster/distance large matrix (fwd)

Dear friends, I have several sets of points in a transformed environmental space. Each set of points can be represented as a cloud in the environmental space. This space is spanned by n coordinates, corresponding to the first n PCs of 36 PCs of some environmental variables (12 monthly minimum temperatures, 12 monthly maximum temperature, 12 monthly precipitations). I would like to calculate

Hey Everyone! I wanted to carry out Hierarchical clustering using distance matrices i have calculated ( instead of euclidean distance etc.) I understand as.dist is the function for this, but the distances in the dendrogram i got by using the following script(1) were not the distances defined in my distance matrices. script: var<-read.table("the distance matrix i calculated",

Hi everyone, I want to create my own distance measure, other than 'euclidean' or 'manhatan', to use in cluster pckgs. To do this I think that I need to change dist(), in mva pckg, or daisy(), in cluster pckg. (or is there a cleaver way ?) But this functions are in fact things like: .Fortran( "daisy", ... ) or .C("dist",...). I tried unsuccessfully to find

Hello, pardon my ingorance, but what distance metric is used in this function in the nnclust package? The manual only says: "Find the nearest neighbours of points in one data set from another data set. Useful for Mallows-type distance metrics." BR, Jay

Hi All, I have a question which does not pertain directly to the use of R but comes from my use of R! I have data which can be described as 3-dimensional e.g. (x,y,z), with no negative component. The suggested way to analyze this data is via multivariate techniques or by calculating what amounts to a levene's test on the data and then an ANOVA on the three components if the first test is

i m using a package NbClust for cluster analysis. in the following algorithm ->NbClust(m, diss="NULL", distance = "euclidean", min.nc=2, max.nc=15, method = "ward", index = "all", alphaBeale = 0.1) i want to define my own dissimilarity matrix of dimension 38*38. my original data "m" is a matrix of 365*38. whenever i define my own dissimilarity

Hi, this is a second try to post this to the R-help mailing list. The first one has been rejected because of a too large attachment. Now I ask this without attaching the data. If you want to reproduce the results, please contact me directly to get the data. (First mail, rejected:) > Attached there is a 149*149 dissimilarity matrix; it is a file obtained by >

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

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 everybody, I have a problem with a cluster analysis. I am trying to use hclust, method=ward. The Ward method works with SQUARED Euclidean distances. Hclust demands "a dissimilarity structure as produced by dist". Yet, dist does not seem to produce a table of squared euclidean distances, starting from cosines. In fact, computing manually the squared euclidean distances from cosines

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 have a dissimilarity matrix X and try to compare it with X' = dist(cmdscsale(X,k)). If I increase k, I should expect that the error (or fit) should monotonically decrease, right. Here is a sample code; library(mva) set.seed(12345) x <- as.matrix(dist(matrix(rnorm(100),ncol=10,byrow=T))) # x[1,2]<-x[2,1]<-1000 ## <<--** 1 # x[5,6]<-x[6,5]<-1000 ##

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

Dear All, a long time ago I ran a cluster analysis where the dissimilarity matrix used consisted of Dmax (or Kolmogorov-Smirnov distance) values. In other words the maximum difference between two cumulative proportion curves. This all worked very well indeed. The matrix was calculated using Dbase III+ and took a day and a half and the clustering was done using MV-ARCH, with the resultant

Sorry, I never use pam. In the help, you can see that pam require a dataframe OR a dissimilarity matrix. If diss=FALSE then "euclidean" was use.So, I interpret that a matrix of dissimilarity is generated automatically. Problems may be in your data. Indeed pam(ruspini, 4)$diss write a dissimilaty matrix while pam(MYdata,10)$diss wite NULL 2017-08-17 16:03 GMT+02:00 Sema Atasever

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") >

Hi, I'm clustering objects defined by categorical variables with a hierarchical algorithm - average linkage. My distance matrix (general dissimilarity coefficient) includes several distances with exactly the same values. As I see, a standard agglomerative procedure ignores this problems, simply selecting, above equal distances, the one that comes first. For this reason the analysis in output

Hi, I'm mighty new to R. I'm using it on Windows. I'm trying to cluster using a distance matrix I created from the data on my own and called it D10.dist. I loaded the cluster package. Then tried the following command... > agnes("E:D10.dist", diss = TRUE, metric = "euclidean", stand = FALSE, > method = "average", par.method, keep.diss = n < 1000,

Hello The excellent adapt package integrates over multi-dimensional hypercubes. I want to integrate over a multidimensional simplex. Has anyone implemented such a thing in R? I can transform an n-simplex to a hyperrectangle but the Jacobian is a rapidly-varying (and very lopsided) function and this is making adapt() slow. [ A \dfn{simplex} is an n-dimensional analogue of a triangle or