similar to: R function calling. Do I understand this right?

Shown below is most of the FORTRAN subroutine named HCLUST. My question concerns the DO loop labeled as '10'. What happened to its CONTINUE statement? I will assume that after FLAG(I)=.TRUE. is executed that control returns to DO 10 I=1,N. Am I correct? Dave ---------------------------- C Initializations C DO 10 I=1,N C We do not initialize MEMBR in order to be able to

Dear all, I need to process large amounts of data (two or three variables for 6,000 cases) cluster analysis. In the end I need to fill the source data to the obtained clusters. I need to trace the sequence of data fusion. In this case, I can fill in a cluster (with any level of linkage distance) by data. This procedure is implemented in the package Statistica, but this package can not work with

Hello list, the following approach did not work: clustersA <- pam(distances, nkA, diss=TRUE); gc(); filenameclu = paste("filenameclu", ".txt"); write.table(clustersA , file=filenameclu,sep=","); although it worked with clustersA <- hclust(distances, method="ward"); and a consecutive kclassA <- cutree(clustersA, k=nkA); filename =

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

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, 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,

Dear All, I am using Silhouette to estimate the number of clusters in a microarray dataset. Initially, I used the iris data to test my piece of code as follows: library(cluster) data(iris) mydata<-iris[,1:4] maxk<-15 # at most 15 clusters myindex<-rep(0,maxk) # hold the si values for each k clusters mdist<-1-cor(t(mydata)) #dissimlarity

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

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

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, In the documentation for agnes in the package 'cluster', it says that NAs are allowed, and sure enough it works for a small example like : > m <- matrix(c( 1, 1, 1, 2, 1, NA, 1, 1, 1, 2, 2, 2), nrow = 3, byrow = TRUE) > agnes(m) Call: agnes(x = m) Agglomerative coefficient: 0.1614168 Order of objects: [1] 1 2 3 Height (summary): Min. 1st Qu. Median Mean 3rd

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 <-

I don't know if there is some fault in compiling or a bug of the new R-1.7.0 version: cl.pam.2 <- pam(as.dist(1-cor(mel.data)),2) plot(cl.pam.2) perform a right partitioning and silhouette plot on the old R-1.6.2 instead "Error in clusplot.default(x$diss,...... ; x is not numeric" is the output on the new R-1.7.0. Same platform: RH8.0 i686. Some suggestions? A.S.

Hi, I am new to clustering and was wondering why pvclust using "maximum" as distance measure nearly always results in p-values above 95%. I wrote an example programme which demonstrates this effect. I uploaded a PDF showing the results Here is the code which produces the PDF file: ------------------------------------------------------------------------------------- s <-

I've been trying to get plot.hclust to work with a hclust object I created and have not had much success. It seems that there is some "hidden" characteristic of a hclust object that I can't see. This is most easily seen in the following example, where plot.hclust works on one object, but when this object is "dumped" and then re-read, plot.hclust no longer works. Is

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 =

Hello, I'm having trouble figuring out how to see resulting groups (clusters) from my hclust() output. I have a very large matrix of 4371 plots and 29 species, so simply looking at the graph is impossible. There must be a way to 'print' the results to a table that shows which plots were in what group, correct? I've attached the matrix I'm working with (the whole thing

Having some problems with using hclust.identify on my data, I revert to trying it out on the example in the help manual. My result is still the same (as with my own data): data(iris) hci <- hclust(dist(iris[,1:4])) plot(hci) testx<-identify.hclust(hci) Error in rect.hclust(x, k = k, x = x$x, cluster = cluster[, k - 1], border = "red") : k must be between 2 and 0

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")