similar to: Clustering and Rand Index

Dear WizaRds, I have been trying to compute the adjusted Rand index as by Hubert/ Arabie, and could not correctly approach how to define a partition object as in my last request yesterday. With package fpc I try to work around the problem, using my original data: mat <- matrix( c(6,7,8,2,3,4,12,14,14, 14,15,13,3,1,2,3,4,2, 15,3,10,5,11,7,13,6,1, 15,4,10,6,12,8,12,7,1), ncol=9, byrow=T )

Dear WizaRds, My goal is to program the VS-KM algorithm by Brusco and Cradit 01 and I have come to a complete stop in my efforts. Maybe anybody is willing to follow my thoughts and offer some help. In a first step, I want to use a single variable for the partitioning process. As the center-matrix I use the objects that belong to the cluster I found with the hierarchial Ward algorithm. Then,

> DATA [,1] [1,] -2.0314185 [2,] -0.3594015 [3,] -1.3225832 [4,] 1.5050892 [5,] -0.4535558 [6,] 3.6335036 [7,] 5.1965750 [8,] 4.2923129 [9,] 6.0913187 [10,] 4.9131070 > RESA [,1] [,2] [,3] [,4] [,5] [1,] -2.0314185 1.5050892 -1.3225832 -1.3225832 -2.0314185 [2,] -0.3594015 -0.3594015 4.2923129 -0.3594015

I need help on calling the svydesign function in the survey package (although this error appears not to be specific to svydesign). I am passing parameters incorrectly but am not sure how to correct the problem. ## Call the main function PS.sim (one of mine). The dots are parameters I omitted to simplify the question. ## y.col, str.col, clus.id, and PS.col are names of columns in the object pop.

Dear people, A somewhat clueless question follows: I just discovered that the lme function in contrib package nlme for R, while similar to the lme function in Splus, does not use the cluster function option. This difference does not appear to be documented in the V&R `R Complements' file. I have data which is divided into 6 groups The lme model is of the form (simplified from the actual

Dear R-listers, I do clustering on tens of individuals by thousands of traits. I have known the assignment of each individual. I want to classify the individuals by randomly resampling different subsets of the traits, for example, randomly resampling 100 traits for 100 times, then 200 traits for 100 times, then 300 traits for 100 times, ,,,,,,. By each subset of traits, I do clustering of the

Hi, can anyone help me fit betabinomial model to the following dataset where each iD is a cluster in itself , if i use package aod 's betabinom model it gives an estimate of zero to phi(the correlation coeficient ) and if i fix it to the anova type estimate obtained from icc( in package aod) then it says system is exactly singular. And when i try to fit my loglikelihood by

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Dear WizaRds, although this might be a trivial question to the community, I was unable to find anything solving my problem in the help files on CRAN. Please help. Suppose I have 4 variables and want to use all possible combinations: 1,2 1,3 1,4 2,3 2,4 3,4 for a further kmeans partitioning. I tried permutations() of package e1071, but this is not what I need. Thank you for your help and

Dear people, I've spent some time trying to find a simple way to do the following. I can certainly think of complicated ways to do it... I have a data set of 99 columns and 2000 rows. Each row corresponds to an individual item of data, each column corresponds to a variable. I want this data to be read into a data frame. The first 66 columns are binary, values 0, 1, and I want these to be

R-helpers: I am using R 2.5 on Windows XP, packages all up to date. I have run into an issue with the MIcombine function of the mitools package that I hoped some of you might be able to help with. I will work through a reproducible example to demonstrate the issue. First, make a dataset from the pbc dataset in the survival package --------------- # Make a dataset library(survival) d <-

Puede que no me haya expresado certeramente. Es lo que pasa al tocar de oído. Tengo unos datos (df) de los que se alguien calculó unas distancias (dis.df) de las que se hicieron/hacen unos clusters (plot.clus.dis.df). Todo esto usando un programa on line al que no tengo acceso para ver cómo hace los cálculos. Yo tengo los datos, y el programa también me da la matriz de distancias. Quiero,

Hi: I have calculated the Silhouette score and Dunn score after hierarchical clustering for 3 clusters: #Distance measure d <- dist(USArrests, method = "euclidean") #Hierarchical clustering hc <- hclust(dist(USArrests), "ave") #calculating silhouette value for 3 clusters sil<- silhouette(cutree(hc, k=3), d) #calculating Dunn index for 3 clusters clus <- cutree(hc,

Hi! I have a question concerning the rcdk package: I generated a test sdf-file with 3 molecules in it and tried to perform clustering by fingerprints and plot the results in a dendogramm. This is what I did: mols <- load.molecules ("molecules.sdf") fp.list <- lapply (mols, get.fingerprint, 'maccs') fp.dist <- fp.sim.matrix (fp.list, method='tanimoto')

Hi Peter, You didn't give a very specific example, but it seems to me that what you wish to do is not really complicated. I suppose you have created a table of sequences vs. say hyprophobicity, charge, etc..., something like... seq hydroph arom b0001 0.104762 0.000000 b0002 0.035122 0.065854 b0003 0.024193 0.070968 b0004 -0.096729 0.084112 b0005 -0.973469 0.091837 b0006

Hi there, I'm trying clustering methods on flow cytometry data. We want to evaluate the clustering results and compare the validation methods. So far the cluster validation functions I found in R are: cluster.stats{fpc} cl_agreement{clue} Are there other validation functions in R? Thank you! [[alternative HTML version deleted]]

Hi All, Mysql service is going down continously in my system due to lack of memory space. I checked the messages log and found the following error message. All mbuf clusters exhausted, please see tuning(7). I have no idea about mbuf cluster. Can anyone please help me to fix the issue. I hope the information below will help you. Following is the output of top. last pid: 84718; load

hi, Excuse me asking three questions in a row for a day, but I had collected those questions as I'm still experimenting with R. This one is how do you compose plots with alot of data in one graph. First what I currently do is after i generated all the data I need to plot.. let say 5-6 arrays I do a plot() and then I created a function to scale the other data to approx fit the initial range so

Hello, In cluster analysis with cluster, how does one colour the silhouette plots ? For example in using pam. There seems to be some problem there. Everything else can be coloured. Thanks, A. Mani Member, Cal. Math. Soc

Maybe the following article is of interest for you (co-authored by P.J. Rousseeuw, like the book you mentioned): http://www.jstatsoft.org/v01/i04/paper/clus.pdf -Heinrich. > -----Urspr?ngliche Nachricht----- > Von: r-help-bounces at stat.math.ethz.ch > [mailto:r-help-bounces at stat.math.ethz.ch] Im Auftrag von Fucang Jia > Gesendet: Donnerstag, 11. Dezember 2003 14:37 > An: