similar to: exporting clustering results to table

Dear Germano, Thank you for your fast reply, In the above code, *MYData *is the actual data set. Do not we need to convert *MYData to *the dissimilarity matrix using *pam(as.dist(**MYData**), k = 10, diss = TRUE*)* code line?* *Regards.* On Thu, Aug 17, 2017 at 2:58 PM, Germano Rossi <germano.rossi at gmail.com> wrote: > try this > > MYdata <-

Sorry, I never use pam. In the help, you can see that pam require a dataframe OR a dissimilarity matrix. If diss=FALSE then "euclidean" was use.So, I interpret that a matrix of dissimilarity is generated automatically. Problems may be in your data. Indeed pam(ruspini, 4)$diss write a dissimilaty matrix while pam(MYdata,10)$diss wite NULL 2017-08-17 16:03 GMT+02:00 Sema Atasever

try this MYdata <- read.csv2("data.txt",dec='.') library(cluster) cluster.pam = pam(MYdata,10) table(cluster.pam$clustering) filenameclu = paste("clusters", ".txt") write.table(cluster.pam$clustering, file=filenameclu,sep=",") 2017-08-17 10:28 GMT+02:00 Sema Atasever <s.atasever at gmail.com>: > Dear Authorized Sir / Madam, > >

[This email is either empty or too large to be displayed at this time]

Dear Authorized Sir / Madam, I have an R script file in which it includes PAM Clustering codes: *when i ran R script i am getting this error:* *Error in pam(d, 10) : x is not a numeric dataframe or matrix.* *Execution halted* How can i fix this error? Thanks in advance. ? data.csv <https://drive.google.com/file/d/0B4rY6f4kvHeCcVpLRTQ5VDhDNUk/view?usp=drive_web> ? *pam.R* data <-

Dear All, I have a data, suppose it is an N*M matrix data. All I want is to classify it into, let see, 3 classes. Which method(s) do you think is(are) appropriate for this purpose? Any reference will be welcome! Thanks! Best, Baoqiang Cao

Dear R users, I discovered something strange using the function agnes() of the cluster package on R 3.0.1 and on R 2.14.1. Indeed, the clusterings obtained are different whereas I ran exactly the same code. I quickly looked at the source code of the function and I discovered that there was an important change: agnes() in R 2.14.1 used a FORTRAN code whereas agnes() in R 3.0.1 uses a C code.

Hi, when I am runnig on samba PDC command: net rpc group add "demo" -Uroot%password , samba returns this message: NT_STATUS _ACCESS_DENIED. What is it? Where shall I set access rights? Thanks Zdenek ____________________________________________________________ http://www.seznam.cz - e-mailov? schr?nka 2000 MB

HI R users With clara function I get a data frame (maybe this is not the exact word, I'm new to R) with the following variables: > names(myclara) [1] "sample" "medoids" "i.med" "clustering" "objective" [6] "clusinfo" "diss" "call" "silinfo" "data" I want to

Hi, I'm mighty new to R. I'm using it on Windows. I'm trying to cluster using a distance matrix I created from the data on my own and called it D10.dist. I loaded the cluster package. Then tried the following command... > agnes("E:D10.dist", diss = TRUE, metric = "euclidean", stand = FALSE, > method = "average", par.method, keep.diss = n < 1000,

Hello all, A question/suggestion: I was wondering if there is a chance of changing stats::cutree to be S3 and use cutree.hclust? For example: cutree <- function(tree, k = NULL, h = NULL,...) { UseMethod("cutree") } cutree.hclust <- stats::cutree # This will obviously need the actual content of stats::cutree This would be nicer for people like me to add new methods to

I try to perform a clustering using an existing dissimilarity matrix that I calculated using distance (analogue) I tried two different things. One of them worked and one not and I don`t understand why. Here the code: not working example library(cluster) library(analogue) iris2<-as.data.frame(iris) str(iris2) 'data.frame': 150 obs. of 5 variables: $ Sepal.Length: num 5.1 4.9 4.7

Hi, this is rather a (presumed) bug report than a question because I can solve my personal statistical problem by working with hclust instead of agnes. I have done a complete linkage clustering on a dist object dm with 30 objects with agnes (R 1.8.0 on RedHat) and I want to obtain the partition that results from a cut at height=0.4. I run > cl1a <- agnes(dm, method="complete")

Hi, this is rather a (presumed) bug report than a question because I can solve my personal statistical problem by working with hclust instead of agnes. I have done a complete linkage clustering on a dist object dm with 30 objects with agnes (R 1.8.0 on RedHat) and I want to obtain the partition that results from a cut at height=0.4. I run > cl1a <- agnes(dm, method="complete")

Dear all, My double for loop as follows, but it is little efficient, I hope all friends can give me a "vectorized" program to replace my code. thanks x: is a matrix 202*263, that is 202 samples, and 263 independent variables num.compd<-nrow(x); # number of compounds diss.all<-0 for( i in 1:num.compd) for (j in 1:num.compd) if (i!=j) { S1<-sum(x[i,]*x[j,])

Greetings, I am reasonably experienced with R but I recently tried to do some clustering using the "cluster" package, in order to see if it would help. I only tried this once with the 1.3.1 version and it worked (I don't quite remember which method I used). Now, I tried with the 1.4.0 version and no clustering function seems to work with matrices that contain NAs, even though

I need to write a nonhierarchical clustering routine and I'm studying the way hclust (in the mva library) is built in R to see how things are done and what I can modify. I ran f2c on the hclust.f file (so I could read it in a language I know!) and there is one thing I don't quite understand about the way it gets called and the way it returns values. That Fortran function gets called in

Hi, I have the distance matrix computed and I feed it to hclust function. The plot function produces a dense dendrogram as well. But, the cutree function applied does not produce the desired list. Here is the code x=data.frame(similarity_matrix) colnames(x) = c(source_tags_vec) rownames(x) = c(source_tags_vec) clust_tree=hclust(as.dist(x),method="complete") plot(clust_tree)

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

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