similar to: clv/cluster - cluster id vector format

Dear list, I have two dissimilarity matrices, one for a training data set which I then clustered using PAM. The second is a diss matrix for a validation data set (an independent field sample). I have been trying to use knn to distinguish distances between the validation data set and the 6 mediods of the training data defined by using PAM. I continue to get error messages in regards to either the

I am having trouble getting a covariance matrix for each cluster which is output by kmeans(). My input looks like: > imagedat <- read.table("table", header=TRUE) > imagedat Red Green Blue 0_0 5 7 8 1_0 5 5 18 2_0 7 8 49 3_0 22 8 76 4_0 54 10 67 5_0 50 9 28 6_0 18 10 15 7_0 9 7 6

Hi all, sorry for this extensive question, but I think that I'm missing something fundamental. I stepped into a surprising result with the integrate function and I would be glad if someone could put some light onto this. I try to integrate over an s-shaped growth-function. The results from this calculation seem to be correct for small values of t. Just for fun I tried some large values and

*Fluidshopping is a Berlin-based startup working on a customer analytics tool for online retailers. Customer Lifefitime Value (CLV) is the mythical 'magic number', the amount of money a particular customer will ever bring in. Knowing your CLV makes it trivial to: - optimize marketing spend for different inbound channels. - identify your highest value customers, - identify those in danger

Hello everyone I tried to use clust function using "hclustWard" clustering method The distance mesaured I have used is "rf" The data used is in the form of data frame (it is not a distance matrix) - sd.df The piece of code I have used is: c1 <- clust(sd.df,k=6,method="hclustWard",distMethod="rf") But I get the following error message, Error in if (n

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, how can i do cluster analysis on spatial data? (longitude & latitude) Because i used the function clust of the clustTool package and it did'nt work at all: cl <- clust(dv,3,method="hclustAverage",distMethod="euclidean") thanks a lot Karine HEERAH Master 2 , océanographie et environnements marins Université Pierre et Marie Curie (Paris 6)

I have a quick question, and I apologize in advance if, in asking, I expose my woeful ignorance of R and its packages. I am trying to use the bootcov function to estimate the standard errors for some regression quantiles using a cluster bootstrap. However, it seems that bootcov passes arguments that rq.fit doesn't like, preventing the command from executing. Here is an example:

Hi I have submitted a package (rioja) to CRAN. It checks OK for all R versions and OS's except r-release-macosx-ix86 where it fails when checking the examples. Specifically, it fails because R can't find the package vegan which is needed in a function. Here is the snippet from the check results: ### Begin snippet checking examples ... ERROR Running examples in 'rioja-Ex.R'

Is it appropriate to use stepAIC (library MASS) with coxph objects (from library survival5) that use "cluster(id)"? It is my understanding that, when using "cluster(id)", we can test for sets of terms by using the methods in Wei et al., (1989; JASA, 84: 1065-1073), or as explained in pp. 53 and ff. of the survival.ps document. But if we use a likelihood ratio test instead

my major is bioinformatics, and i'm trying to cluster ( agglomerate the closest pari of observations ) in R. i have already got my own similarities metric, but do not know how to clust it based on similarities instead of dissimilarities. since the help document of hierclust mentions the parameter "sim", which seems good to me, but it doesn't appear in the code of hierclust()

I'm running Mclust 3.0 in R-2.7.0 and have run into a situation where the optimal number of clusters, 12, seems to be greater than the number of colors available to Mclust. The code below, which demonstrates errors, does not if the max no of clusters is set at 10. sessionInfo, after a restart of R and loading of packages, follows the output. Is this a known problem with Mclust? Can I

Hi, I am currently analysising a counting process form of a cox model allowing for the inclusion of time dependent covariates. An example model I have fitted is modlqol<-coxph(Surv(Tstart,Tstop,cens.time)~tmt.first+risk +lqol+cluster(id),data=cat) summary(modlqol) My question is quick. I am looking at 1 event (death), and repeated measurements (the time dependent covariate 'lqol')

Dear R users, I'd like to ask u whether you know how to sort out the following: I'm trying to reproduce a dataset of clusters, and for that I need to build up a cluster index (inside a function) using the command "replicate" as follows: dataset<- function( clusters=100, cluster.size=50, outcome.mean=list(mean=3, sd=1), outcome.var=list(mean=0, sd=1), treat=1, unbal =

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,

Hello, I would like to cut a hclust tree into several groups at a specific similarity. I assume this can be achieved by specifying the "h" argument with the specified similarity, e.g.: clust<-hclust(dist,"average") cut<-cutree(clust,h=0.65) Now, I would like to draw rectangles around the branches of the dendrogram highlighting the corresponding clusters, as is done by

Hello, I am fairly new to R, but I am not new to programming at all. I want to generate random clusters in a 1,000x1,000 box such that I end up with a total of about 2,000 points. Once done, I need to export the X,Y coordinates of the points. I have looked around, and it seems that the spatstat package has what I need. The rMatClust() function can generate random clusters, but I have run into

Hello, The following code does'nt work for me. The last command reports an error. I have created a consensus tree using the consensus comand from phylo but cannot manipulate the phylo object afterwards to create a dendogram , by transforming the phylo object into a hclust object and then into a dendogram ?? Thanks for any help library(ade4) library(cluster) library(stats) library(ape)

John, You can make a local version of bootcov which either: deletes these arguments from the call to fitter, or modify the switch statement to include rq.fit, the latter would need to also modify rq() to return a fitFunction component, so the first option is simpler. One of these days I'll incorporate clustered se's into summary.rq, but meanwhile this seems to be a good alternative.

Hi all, once more about the old subj :-) My data has too much various distribution families and for every particular experiment I need just to decide whether the data is "quite homogeneous" or it has two or more clusters. I've revisited the following libraries: amap, clust, cclust, mclust, multiv, normix, survey. And I didn't find any ready-to-use general