search for: clusted

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 =

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

Hi R-helpers I have a dataframe DF (lets say with the variables, y, x1, x2, x3, ..., clust) containing relatively many NAs. When I fit an ordinal regression model with the function lrm from the Design library: model.lrm <- lrm(y ~ x1 + x2, data=DF, x=TRUE, y=TRUE) it will by default delete missing values in the variables y, x1, x2. Based on model.lrm, I want to apply the robust covariance

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

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

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)

Hello, I have a large data set that has more columns than rows (sample data below). I am trying to perform a partitioning cluster analysis and then plot that using pca. I have tried using CLUSPLOT(), but that only allows for 'princomp' where I need 'prcomp' as I do not want to reduce my columns. Is there a way to edit the CLUSPLOT() code to use 'prcomp', please? #

Hi: I have tried to find the appropriate clust algorithm for mixed type of data. The suggested way I see is: 1. use daisy to get the dissimilarity matrix 2. use PAM/hclust by providing the dissimilarity matrix, to get the clusters but by following this, when the data set grows bigger say 10,000 rows of data, the dissimilarity matrix will be O(n^2), and out of memory will occur. I am

Problem with join to Active Directory [root@clust-master samba]# net ads join -S 10.0.0.1 -U Administrator Administrator's password: [2006/05/22 10:24:05, 0] libads/ldap.c:ads_join_realm(1640) ads_add_machine_acct (clust): Type or value exists ads_join_realm: Type or value exists [root@clust-master samba]# kinit Administrator@COROD.LOCAL Password for Administrator@COROD.LOCAL: As you can

Hi, I was wondering if there is more flexibility in the output of dendrograms when plotting a hclust object. I can't seem to find information on how to change the default output of a "hanging" style tree with the axis on the right to a left-to-right plot with and axis on the bottom. Example code follows: library(vegan) #loads the "vegan" module that compuptes ANOSIM

Hello, Can someone point me to documentation or ideas on how to calculate the centroids of clusters identified with hclust ? I would like to be able to chose the number of clusters (in the style of cutree) and then get the centroids of these clusters. This seems like a quite obvious task to me, but I haven't been able to put my hands on a relevant command. Thank you, Moritz

Hi the list, I am writing a package in S4 and I do not manage to understand a bug. The "R CMD check" and the "R CMD build" both work. Here is links to the package (not on CRAN yet for the raison that I explain bellow): http://christophe.genolini.free.fr/aTelecharger/kml_0.5.zip http://christophe.genolini.free.fr/aTelecharger/kml_0.5.tar.gz Then I install the package and I

Hallo, I have two servers and both are connected to external array, each by own SAS connection. I need these servers to work simultaneously with data on array and I think that ocfs2 is suitable for this purpose. One server is P4 Xeon (Gentoo linux, i386, 2.6.22-r2) and second is Opteron (Gentoo linux, x86_64, 2.6.22-r2). Servers are connected by ethernet, adapters are both Intel

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'

hi all, i'm trying to plot a dendrogram with labeled leaves >rownames(f)<-v.names >v<-rowMeans(f, na.rm=T) >clust<-hclust(dist(v)) >dend<-as.dendrogram(clust,hang=0.05) >clust2<-cut(dend, h=0.5) >class(clust2$low[[1]]) >[1] "dendrogram" then >plot(clust2$low[[1]],horiz=TRUE,frame=F,type = "tr")) but my leaf labels do not fit

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:

I have survey data that I am working on. I need to make some multi-way tables and regression analyses on the data. After attaching the data, this is the code I use for tables for four variables (sweight is the weight variable): > a <- xtabs(sweight~research.area + gender + a2n2 + age) > tmp <- ftable(a) Is this correct? I don't think I need to use the strata and cluster

Hi all, When setting auth mechanisms: plain login cram-md5 and only having pam passdb and passwd userdb. dovecot nevers starts and crashes reporting: Feb 29 10:19:41 pitux-clust-op1 dovecot: Dovecot v1.0.10 starting up Feb 29 10:19:41 pitux-clust-op1 dovecot: auth(default): CRAM-MD5 mechanism can't be supported with given passdbs Feb 29 10:19:41 pitux-clust-op1 dovecot: Auth process died

ESTE SCRIPT FUNCIONA PERFECTAMENTE, LA PREGUNTA QUE HACÍA SE REFERÍA A QUE SI SERÍA POSIBLE QUE EN LA PROPIA TABLA CSV PUDIERAN ESTAR LOS NOMBRES DE LOS ANTIBIOTICOS Y QUE EL ANÁLISIS PCA NO LO RECHACE POR SER CUALITATIVO, QUE DE ALGUNA MANERA SE DIERA UNA INSTRUCCIÓN PARA EVITAR TENER QE HACER LO DE rownames manualmente setwd("D:/Public/Documents/R/FactoMineR/") table1 <-

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