similar to: Var problem

First thanks to the fast answer regarding the "Clustplot problem"... 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)) in 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

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

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

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

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

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 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.

How can I control de "wrap-around" color behaviour in the persp function ? I am using something like : persp(bb[1:100,2:97], col= rainbow(8,start=0.1, end=0.8))) Depending on the rainbow length value I get several "wrap-around" blocks of the selected color range...something that I wanted to avoid... My idea is to use the color in order to make a separation from a certain

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? #

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

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 am a relative novice with R and am having some difficulty using 'clusplot' (package Cluster). I have performed PCA analysis (using vegan) on a large set of morphometric measurements and revealed up to 4 principal components. To examine the grouping of the data I have used PAM followed by clusplot to visualise the clusters. My problem is that I would like to see the clusters plotted on

I created a clusplot from PAM results. It represents how signals have been classified. Signals are identified by a numerical label. My trial distance matrix is made up of 10 rows, one for eacjh signal. I assigned the signals iidentifiers as rownames of the distance matrix. rwn [1] "1104" "1332" "2057" "2425" "2483" "2530"

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)

On 03/19/2017 01:01 AM, Thomas Schmitt via Syslinux wrote: > David Christensen wrote: >> Is there a way I can build live 32-bit Debian GNU/Linux USB images without >> isohdpfx.S and syslinux.bin? > > You could try > https://www.debian.org/releases/jessie/i386/ch04s03.html.en#usb-copy-flexible This is how I prepared an ADATA USB Flash Drive 4 GB: # cat

Previously we were treating any indirect temp array usage to mean that everything should end up in lmem. The MemoryOpt pass would clean a lot of that up later, but in the meanwhile we would lose a lot of opportunity for optimization. This helps a lot of Metro 2033 Redux and a handful of KSP shaders: total instructions in shared programs : 6288373 -> 6261517 (-0.43%) total gprs used in shared

Hi there, I want to do classify some 2-dimensional points into four clusters by pam() in the cluster package. However, I encountered some problems. 1. How can I change the "xlab" and "ylab" instead of the default "Component 1" and "Component 2"? When I put "xlab" option in the function, it always says "formal argument "xlab"