similar to: Use of the command 'replicate'

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

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? #

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

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 written a wrapper for heatmap.2 called heatmap.w.row.and.col.clust which auto-generates breaks using breaks<-round((c(seq(from=(-20 * stddev), to=(20 * stddev))))/20, digits = 2) #(stddev in this case = 2.5) This has always worked well in the past but now I am getting an error that non-finite breaks are being generated. Drilling down, it seems that my wrapper is generating finite

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

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

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 <-

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

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: 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

I just downloaded the NT version of R and cannot find anything concrete on loading packages. There is alot on how to do it under Unix. The windows FAQ briefly mentions it but does not explain it. I have donwloaded windows binaries of the package "clust.zip" and unzipped it into my c:\rw0632\library directory... So I have c:\rw0632\library\cluster Now what do I do ?

Dear All, I would like to use the simprof function (clustsig package) but the available distances do not include Jaccard distance, which is the most appropriate for pres/abs community data. Here is the core of the function: > simprof function (data, num.expected = 1000, num.simulated = 999, method.cluster = "average", method.distance = "euclidean", method.transform =

Dear R-helpers, Happy New Year to all the helpful members of the list. Here is the behavior I'm looking for: > v1 <- c("a","b","c") > n1 <- c(0, 1, 2) > v2 <- c("c", "a", "b") > n2 <- c(0, 1 , 2) > (f1 <- data.frame(v1, n1)) v1 n1 1 a 0 2 b 1 3 c 2 > (f2 <- data.frame(v2, n2))