similar to: Dendrogram plotting options?

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

Hi, My name is Mauro Belleggia, I m doing an ANOSIM (using "vegan" package) to test statistically whether there is a significant difference between 3 (1,2 and 3) groups of sampling units. My results were: Call: anosim(dis = distancias, grouping = Lt) Dissimilarity: bray ANOSIM statistic R: 0.1313 Significance: < 0.001 Based on 1000 permutations Empirical upper confidence

I'd like to change the way plot.hclust displays an hclust object. Here's a description of how it's done now, from the R documentation of hclust: In hierarchical cluster displays, a decision is needed at each merge to specify which subtree should go on the left and which on the right. Since, for n observations there are n-1 merges, there are 2^{(n-1)} possible

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, 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

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 am interested in using ANOSIM (analysis of similarities) which is part of the vegan package. When I look at the programmers notes they mention that "They don't quite trust this method, and someone should study it closely." I was just wondering if this had been done, and whether it had been found reliable or not? Thanks, Kerry Reeves

Hello all, I used google and looked at the documentation to find out why the ADONIS function is called adonis (in the vegan package). I am writing a document and would like to include a abbreviation list (similar to "ANOSIM = Analysis of Similarities"). regards, Steve

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

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

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 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

Dear vegan team, Vegan development happens now completely in github. R-Forge repository is no more in sync with github. I tried to commit all github changes to R-Forge, but a week ago I got a conflict in file and I haven't had time to resolve that conflict. You can follow vegan development and vegan discussion also without signing to github. The system seems to be completely open and does not

Dear all, I generated a heatmap with the heatmap() function with the default "complete" linkage method, but when I add the argument Rowv=as.dendrogram(hclust(dist(matrix), method="complete")), The dendrogram generated is different from the dendrogram produced by heatmap() with default settings. Could someone kindly point out where my mistakes are? P.S. When I did

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'

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 <-

When I use the function cutree(), the numbers of the clusters are not in the same order as the plotted dendrogram. Is there any way of sorting them so that they match the tree? Is it possible to plot a dendrogram horizontally, preferably with the branches to the right? This would enable some practical composite plots, e.g. labels that were an entire table with several columns of information, or