similar to: Help Var passing in function

> Regarding a new problem: > for (i in 1:5) { z <- clara(adata, i) plot(z) } > in the above code in the plot screen > I get something like: clusplot(clara(x=adata,i)) as title; > in the 2nd type of plot I get > silhouete plot of clara(x=adata,k=i,samples=50) > > How can I pass the real value to the i? > What kind of command

Hello, I have a large data matrix (68x13112), each row corresponding to one observation (patients) and each column corresponding to the variables (points within an NMR spectrum). I would like to carry out some kind of clustering on these data to see how many clusters are there. I have tried the function clara() from the package cluster. If I use the matrix as is, I can perform the clara

I compiled R 2.1.0 under Windows XP SP2 as a preliminary to rebuilding a custom package for use with R 2.1.0. The compile completed successfully, and I was able to run demo(graphics) successfully. But make check and make check-recommended fail. > version _ platform i386-pc-mingw32 arch i386 os mingw32 system i386, mingw32 status major 2 minor 1.0 year 2005

Dear R-list members, I am using the cluster package and by the generation of plot.partition I ran into the problem that an alternative title overlaps the default title. > plot.partition(clara.14,which.plot=2,stand=TRUE, main="Silhouette plot of 14 clusters") The manual states that all optional arguments for clusplot.default may also be supplied to plot.partition(). Altering the

Sometimes I have NA values within specific columns of a dataframe (in this example, the first two columns can have NAs). If there are NA values, I would like them to be removed. I have been using the code: y<-c(NA,5,4,2,5,6,NA) z<-c(NA,3,4,NA,1,3,7) x<-1:7 adata<-data.frame(y,z,x) adata<-adata[-which(apply(adata[,1:2],1,function(x)any(is.na(x)))),] This works well if there are NA

I have two more or less parallel dataframes that are giving me different results on one subset of variables. I know that I assembled the 2 dataframes slightly differently but I don't see why I am getting this result because one set of variables are labelled and the other is not. Variable names are the same, etc. as far as I can acertain. The only diffference seems to be that bdata variables

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? #

Dear all I'm trying to run a cluster analysis with R Here are the commands: mydata <- na.omit(matrix) # listwise deletion of missing mydata <- scale(matrix) # standardize variables fit <- kmeans(mydata, 8) # 8 cluster solution # get cluster means aggregate(mydata,by=list(fit$cluster),FUN=mean) # append cluster assignment mydata <- data.frame(mydata, fit$cluster)

Dear R folk: Perhaps I'm just dense today, but I am having trouble reproducing the principal components plotted and summarized by clusplot. Here is a brief example using the pluton dataset. clusplot reports that the first two principal components explain 99.7% of the variability. But this is not what princomp is reporting. I would greatly appreciate any advice. With best regards, -- Tom

I have "small" problem ... with the cluster library each time I try to use the "agnes","pam","fanny" functions with more than 20000 elements I get the following error: >Error in vector("double", length) : negative length vectors are not allowed >In addition: Warning message: >NAs introduced by coercion But with the clara

Doing the normal build process [1] for a first time with a R 2.0.0 snapshot -- the Sep 21 version I uploaded to Debian's 'experimental' section two days ago, ended in failure. The package in question is cluster 1.9.6 which should be 2.0.0-ready. The (partial) log follows: ----------------------------------------------------------------------------- [...] g77 -mieee-fp -fPIC -g -O2

Hi dear all It's the first time for me to install a developmental version of R, I came across following errors, my system is DISTRIB_ID=Ubuntu DISTRIB_RELEASE=10.04 DISTRIB_CODENAME=lucid DISTRIB_DESCRIPTION="Ubuntu 10.04.1 LTS" I downloaded the dev version of R in cran R source, and downloaded the recommended packages by using wget as described in R manual, and run

The following code in clusplot.default (package cluster) is in error: x1 <- cmdscale(x, k = 2, eig = TRUE) var.dec <- sum(x1$eig)/sum(diag(x1$x)) if (var.dec < 0) var.dec <- 0 if (var.dec > 1) var.dec <- 1 x1 <- x1$points x1 has components with names "points" and "eig", not "x", so

Hi, I have a gluster volume that consists of 22Bricks and includes a single folder with 3.6 Million files. Yesterday the volume crashed and turned out to be completely unresposible and I was forced to perform a hard reboot on all gluster servers because they were not able to execute a reboot command issued by the shell because they were that heavy overloaded. Each gluster server has 12 CPU cores

Dear R-list, I am in the process of translating a long function written in Matlab into R (mainly because I am a big of fan of R, and folks will not have to pay to use it :). In the translation of this function I got stack because they use spdiags, which, as far as I can tell it is not available in R. I have explored the Matrix package, from which I borrowed some of the functions (e.g.,

Dear R Users, It works fine. while i read the dataset from the text file. (refered R-data import -export*.pdf) > read.table("c:\\DataES.txt") V1 V2 1 ACW261 33 2 ACW311 7 3 ACW321 31 4 ACW342 39 5 6 7 Now my question is how do i /*convert text file Data into *.RData* which i can use as a dataset/data.matrix whereby i can use it for clusplot. Thanks in Advance, JJ ---

Hello everyone. I just started learning R for clustering analysis in my research project. I tried k-means method and PAM method, both of which were properly processed with my data. I have some questions about PAM graphical output. Suppose to do the commands shown below; pm<-pam(D, 6) plot(pm) I got two charts after prompted. In the first chart, 6 oval clusters are drawn together

Dear people, I used clusplot to plot a partition result. The partition result was from pamk with a distance object as input. Then I applied cmdscale on the same distance object for coordinates to make another scatterplot. My problem is this: the coordinates from the cmdscale calculation, though with the same shape, were different in scale and rotation from the scatter plot yielded by clusplot.

Hi I was using rsync with the following options rsync -aPH --exclude-from=/var/tmp/ignorelist /home/me/ /run/media/me/ADATA\ #2/rsync-me/ and I was creating a copy of a home directory on my Linux machine. It went along for sometime (some 30,000 files) when it stopped with the following message. ---> Desktop/OSme/Web-Site-Backups/backup-5.4.2014_15-11-43_linux014.tar.gz 4288446464 78%

Dear R user, I am a biochemist/bioinformatician, at the moment working on protein clusterings by conformation similarity. I only started seriously working with R about a couple of months ago. I have been able so far to read my way through tutorials and set-up my hierarchical clusterings. My problem is that I cannot find a way to obtain information on the rooting of specific nodes, i.e. of