similar to: for "bclust" in package "e1071"

Hi everyone, Currently i'm trying to understand the bagged clustering algorithm, bclust {e1071}. When I run the given example in the help file (as below) data(iris) bc1 <- bclust(iris[,1:4], 3, base.centers=5) plot(bc1) and plot the bclust object, 2 graphs are produced. The first is a dendrogram, but what is the second plot? The axes are not labelled and what do the two

Hello everybody, I have a problem with the commando of boxplot -bclust. http://127.0.0.1:13155/library/e1071/html/boxplot.bclust.html > data(iris) > bc1 <- bclust(iris[,1:4], 3, base.centers=5) Committee Member: 1(1) 2(1) 3(1) 4(1) 5(1) 6(1) 7(1) 8(1) 9(1) 10(1) Computing Hierarchical Clustering > boxplot(bc1) Warnmeldungen: 1: In if (x$datamean) { : Bedingung hat Länge > 1 und

Dear all, I have a problem with the function "bagged clustering" of package e1071. When I try to run the example of bagged clustering with the iris data : data(iris) bc1 <- bclust(iris[,1:4], 3, base.centers=5) I got the following message error : Loading required package: class Committee Member: 1(1) 2(1) 3(1) 4(1) 5(1) 6(1) 7(1) 8(1) 9(1) 10(1)Error in bclust(iris[, 1:4], 3,

Dear All: I'm a newbie to R and chemometrics. Now I'm trying apply bclust on fuzzy c-means like this: >bc1 <- bclust(iris[,1:4], 3, base.centers=20,iter.base=100, base.method="cmeans") Committee Member: 1(1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)Erro r in bclust(iris[, 1:4], 3, base.centers = 20, iter.base = 100, base.method =

Hi All, For the function "bclust"(e1071), the argument "base.method" is explained as "must be the name of a partitioning cluster function returning a list with the same components as the return value of 'kmeans'. In my understanding, there are three partitioning cluster functions in R, which are "clara, pam, fanny". Then I check each of them to

Hi All, Just want to make sure, in function "bclust", do the following argument only have one option? argument "dist.method" has one option "Euclidian"; argument "hclust.method" has one option "average"; argument "base.method" has one option "kmeans". Thank you! [[alternative HTML version deleted]]

Hi, I have a very large dataset of 3008 individuals and 800 numerical variables. In fact it is a table of 3008 36-monthes multivariated time series that I would like to classify with an unsupervised algorithm I had a look at the function kkmeans of e1071 package, which seems to be a kernel weighted version of the algorithm algorithm, and the bclust from the same package which does bootstrapping

Hi All, I run function "kmeans" to cluster a matrix. But when the matrix size is big enough, it keeps saying "did not converge in 10 iterations". Could you explain what it means and if the result is wrong? And the interesting thing is sometimes this warning happens when the sample size is around 51200 x 6, sometimes it happens around 30000 x 6. Does the warning related

A new version of e1071 has been released to CRAN which should be much easier to install on a lot of platforms because reading/writing PNM images has been moved to the pixmap package, hence there are no longer dependencies on external libraries and no configure mechanism. For the authors, Fritz Leisch ********************************************************** Changes in Version 1.2-0: o

Dear list, I am facing a problem in my statistical analyses on R. My experiments are about plants, I record there growth after each cutting (every 3 weeks). 'BC' is for the plant, and '1' to '5' is the time of cutting and recording. The data and R script are : "" BourdCoup <- c(21, 7.2, 9.2, 0, 8.52, 14.7, 8.31, 6.2, 127.05, 115.2, 100.7, 24, 162.64, 136.8,

Dear R users I use Windows XP, R2.5.1 (I have read the posting guide, I have contacted the package maintainer first, it is not homework). In a research project on renal cell carcinoma we want to compute Harrell's c index, with optimism correction, for a multivariate Cox regression and also for some univariate Cox models. For some of these univariate models I have encountered an error

i'm having this problem: https://bugzilla.samba.org/show_bug.cgi?id=2220 But. i'm running samba 3.0.24 not 3.0.11 as in bug page. take a look: # smbd -b Build environment: Built by: brewbuilder@hs20-bc1-7.build.redhat.com Built on: Wed Feb 7 23:52:44 EST 2007 Built using: gcc Build host: Linux hs20-bc1-7.build.redhat.com 2.6.9-34.ELsmp #1 SMP Fri Feb 24 16:56:28 EST

I use kmeans to classify spectral events in high and low 1/3 octave bands: #Do cluster analysis CyclA<-data.frame(LlowA,LhghA) CntrA<-matrix(c(0.9,0.8,0.8,0.75,0.65,0.65), nrow = 3, ncol=2, byrow=TRUE) ClstA<-kmeans(CyclA,centers=CntrA,nstart=50,algorithm="MacQueen") This works well when the actual data shows 1,2 or 3 groups that are not "too close" in a cross plot.

Dear List, I am having trouble with a tricky merging task. I have one data sheet that has dates (continuous) that radio collared individuals were monitored via telemetry. I have a different sheet containing data from instances where individuals were recaptured and associated body condition data was recorded (sheet 2). I would like to merge the two sheets by individual and date (I can do this

Dears Sirs During my computational work I encountered unexpected behavior when calling "ar" function, namely # time series x<-ts(c(-0.2052083,-0.3764986,-0.3762448,0.3740089,0.2737568,2.8235722,- 1.7783313,0.2728676,-0.3273164),start=c(1978,3),frequency=4,end=c(1980,3)) # ar function res.ar<-ar(x,aic=TRUE,demean=F) # call "ar" again and ............

Hi there, I notice that some of the clustering methods in R are not appropriate to deal with large data set. Here is the list I make to see which are appropriate or which are not appropriate for large dataset. Could you please take a look and check if it is right or not? I need this information to decide which methods I should choose. Thank you! P.S.: List: Appropriate for large

Hi, I wish to build consensus groups/tree from a set of bootstraps from a clustering algorithm such as hc or k-means, but can't find an R-function that does this. Does anyone know of an R procedure/function which allows one to build such consensus groups/tree .? Many thanks, Andrew ******************************************************************* Dr Andrew E Teschendorff Hutchison/MRC

Greetings, I have had good success using the clara() function to perform a simple cluster analysis on a large dataset (1 million+ records with 9 variables). Since the clara function is a wrapper to pam(), which will accept known medoid data - I am wondering if this too is possible with clara() ... The documentation does not suggest that this is possible. Essentially I am trying to

Dear all, I'm trying to estimate clusters from a very large dataset using clara but the program stops with a memory error. The (very simple) code and the error: mydata<-read.dbf(file="fnorsel_4px.dbf") my.clara.7k<-clara(mydata,k=7) >Error: cannot allocate vector of size 465108 Kb The dataset contains >3,000,000 rows and 15 columns. I'm using a windows computer

Dear Clara, Thanks for the reply. I am forwarding your message to the list, ok. When I wrote was a way of get further information to help the helpers. happy holidays, milton ---------- Forwarded message ---------- From: Clara Brück <clara_brueck@web.de> Date: 2009/12/30 Subject: Re: [R] Negbin Error Warnings To: milton ruser <milton.ruser@gmail.com> Dear Milton, Thanks for