similar to: Obtaining covariance matrices for kmeans output clusters

Hi, I am trying to run a 1D nutrient-phytoplankton-zooplankton model in R using the package 'deSolve'. The code is shown below: DEPTH = seq(2.5, 147.5, 5) NPZ = function(t, state, params){ with(as.list(params), { P <- state[1:NB] Z <- state[(NB + 1): (2*NB)] N <- state[(2*NB + 1): (3*NB)] F.I = function(z, hr){ I0 = function(hr){

Dear R-users, I'm running an MLE procedure where some ODEs are solved for each iteration in the maximization process. I use mle2 for the Maximum likelihood and deSolve for the ODEs. The problem is that somewhere along the way the ODE solver crashes and I get the following error message: DLSODA- Warning..Internal T (=R1) and H (=R2) are such that in the machine, T + H = T on the next

Hi All, A question from a newbie using R 2-5-0 on windows XP. Why is it that kmeans clustering with apparently the exact same parameters behaves so differently between the two following examples : > cl1 <- kmeans(subset(pointsUXO15555, select = c(2:4)), 10) Takes about 2 seconds to deliver a result > cl1 <- clust(subset(pointsUXO15555, select = c(2:4)), k=10,

Dear R Experts, I am running a cluter analysis using kmeans and have come across an error to which I am unable to find a solution. First, let me describe the problem: THE R CODE IS: -------------------------------------- # NRM is a 100 x 100 numerical matrix infile = 't:\\NRM\\NRM' groups = 7 outfile = 't:\\NRM\\cluster.groups' print( paste(infile, groups, outfile, sep='

Dear All, Is there any existing code for plotting silhouette for kmeans clustering results? Many thanks! Linda [[alternative HTML version deleted]]

Dear R, Can't I cluster a dataset into k clusters where k is exactly the number of observations? I have version 12.2 installed. See this example > a <- matrix(1:100, 20) > kmeans(a, 20) Error: number of cluster centres must lie between 1 and nrow(x) This is a bit ad-hoc but I known R from version 2.12 allows number of clusters to be one. So I guess allowing number of clusters to be

Dear helpers I'm sorry to insist but I still think there is something wrong with the function kmeans. For instance, let's try the same small example: > dados<-matrix(c(-1,0,2,2.5,7,9,0,3,0,6,1,4),6,2) I will choose observations 3 and 4 for initial centers and just one iteration. The results are > A<-kmeans(dados,dados[c(3,4),],1) > A $cluster [1] 1 1 1 1 2 2 $centers

I am trying to cluster some binary data using k-means . As the regular "kmeans" available from stats package in R does'nt provide the option to change the distance method. I was wondering there is any package available to specify type of distance measure to be used in k means clustering in R. Especially distances like "Jaccard" which is good for binary data.

Hello, I have been trying for a few days to do kmeans on a matrix of data which I populate from java. Here's my code: String[] Rargs = {"--vanilla"}; Rengine re = new Rengine(Rargs, false, null); System.out.println("Rengine created, waiting for R"); // the engine creates R is a new thread, so we should wait until it's // ready if

Dear all, I need to know in which number of iterations the kmeans converge each time I run it. Any idea how to do it? Thank you for your attention, Rui

Hi R users! I have a bit of a problem with using an hierarchical clustering algorithm: a<-c(1:15) b<-rep(seq(1:3), 5) c<-rnorm(15, 0,1) d<-c(sample(1:100, 15, replace=T)) e<-c(sample(1:100, 15, replace=T)) f<-c(sample(1:100, 15, replace=T)) data<-data.frame(a,b,c,d,e,f) q<-data.frame(data$d, data$e, data$f) q<-scale(q) What i want to do is to use an

Buenas tardes. Quisiera saber como puedo aplicar las reglas de un kmeans a otra base, para hacer un proceso de validaciòn de la segmentaciòn. En SAS, se cuenta con el argumento "OUTSEED", ¿què debo tener en cuenta en R? Mil gracias. -- Luis Alberto López González [[alternative HTML version deleted]]

I know that within sum of squares, DB, sillhouette and cophenetic are indicators of clustering quality, but what indicators I need to observe when I choose attributes for kmeans? -- View this message in context: http://r.789695.n4.nabble.com/Feature-selection-for-kmeans-tp4657830.html Sent from the R help mailing list archive at Nabble.com.

Hello, I am using the function mda of the mda library in order to discriminate 4 groups with 8 explanatory variables. I only have 66 observations. I tested all possible combinations of those variable and run for each the Mixture Discriminant Analysis. For some iterations, I got an error message: "error in kmeans(xx, start): initial centers are not distinct". I understood that the

I've gone through the link that you sent me and I currently understand how this helps and works to some extent, but I am not too sure of how I should start with converting the current interface to PIMPL design. I'm not used to this design pattern so its taking some time to sink in :) Say I start with the Clusterer class, I create a ClustererImpl class which is the internal class that

Sir, Would like to know what sort of input matrix are taken by the kmeans function defined in mva library of R application. As per the documentation for the Kmeans it takes the following 2 data sets: 1) data 2) centers The commands to be executed in R are as follows: library(mva) data <- read.table('file1',header=TRUE,sep="\t") centers <-

Dear helpers I was working with kmeans from package mva and found some strange situations. When I run several times the kmeans algorithm with the same dataset I get the same partition. I simulated a little example with 6 observations and run kmeans giving the centers and making just one iteration. I expected that the algorithm just allocated the observations to the nearest center but think this

Hello, I have been getting the following intermittent error from kmeans: >str(cavint.p.r) num [1:1967, 1:13] 0.691 0.123 0.388 0.268 0.485 ... - attr(*, "dimnames")=List of 2 ..$ : chr [1:1967] "6" "49" "87" "102" ... ..$ : chr [1:13] "HYD" "NEG" "POS" "OXY" ... > set.seed(34) >

I am a complete beginner at R and am using the "kmeans" function for the first time... I have a data frame (dat) that is 17 columns * 91 rows (including headers) I have entered the following: cl <- kmeans(dat, 3, 10) plot(dat, col = cl$cluster) points(cl$centers, col = 1:2, pch = 8) the output is 17*17 graphs ... this is not what I was anticipating - I was hoping to get one graph

Hello, I would like to have any help with the function Kmeans of R.. I use this to do a classification of my data...I have chosen 12 classes but, I have always an error message: Error: empty cluster: try a better set of initial centers So, I don't understand the probleme with this function.. Thank you to help me!! All the Best Clothilde Clothilde Kussener CNRS - CEBC 79360 Villiers en bois