similar to: labels in cluster pam plot

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, > >

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 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 <-

Dear WizaRds, My goal is to program the VS-KM algorithm by Brusco and Cradit 01 and I have come to a complete stop in my efforts. Maybe anybody is willing to follow my thoughts and offer some help. In a first step, I want to use a single variable for the partitioning process. As the center-matrix I use the objects that belong to the cluster I found with the hierarchial Ward algorithm. Then,

On Sun, Mar 6, 2016 at 7:17 AM, James Aylett <james-xapian at tartarus.org> wrote: > On Sat, Mar 05, 2016 at 10:58:43PM +0530, Richhiey Thomas wrote: > > K-Means or something related certainly seems like a viable approach, > so what you'll need to do is to come up with a proposal of how you'd > implement this in Xapian (either with reference to the previous work, >

Dear R Users: I am new to R and I am trying to do a cluster analysis on a single continuous variable using the Agnes [Agglomerative Nesting (Hierarchical Clustering) ] in the Package ‘cluster’. I was able to apply this clustering method to my data: ward1 <- Agnes(balances, diss= FALSE, metric = "euclidean", stand = TRUE, method = "ward", keep.diss =TRUE, keep.data =

Hello R-helpers I want to put a color scale in a plot: I've got an xy plot where the values of the response (z=f(x,y)) is symbolically given by colors (like heat or rainbow color scale) I would like to put such a scale with apprpriates labels in the plot, so as to facilitate the interpretation (like in a finite elements result plot) How is taht possible? Thanks Anne [[alternative

Hello, Can someone point me to documentation or ideas on how to calculate the centroids of clusters identified with hclust ? I would like to be able to chose the number of clusters (in the style of cutree) and then get the centroids of these clusters. This seems like a quite obvious task to me, but I haven't been able to put my hands on a relevant command. Thank you, Moritz

Hello! After a presentation of some statistical analysis of process datas, (where the few R possibilities I was able to show made quite a big impression), I was asked if it was possible to program a statistical application which could be used directly by the end user. Such an application would include a userfriendly interface (developped in C++), a db , a core statistical program, standard

Hello, I'm new to R and in the process of testing it My first question: I fail to read in my data (ANSI toto.txt file, tab separated) > test <-read.table("toto.txt") Error in file(file, "r") : unable to open connection In addition: Warning message: cannot open file `toto.txt' > test <-

Hi! Is there an easy way to add to the scree-plot labels to each value pertaining to the cumulative proportion of explained variance? Thanks and a happy new year Anne ---------------------------------------------------- Anne Piotet Tel: +41 79 359 83 32 (mobile) Email: anne.piotet@m-td.com --------------------------------------------------- M-TD Modelling and Technology Development PSE-C

Hi There, I'm working on some cluster analyses on a large data-set using hclust with Wards method and Manhattan (city block) distance measures. I've created dendrograms to illustrate the clustering criteria, but would like to create a plot to examine for the classic elbow criterion to use in determining the best number of clusters. Ideally I'd like to plot percent variance explained

On Mon, Mar 14, 2016 at 02:09:13AM +0530, Richhiey Thomas wrote: > The way the paper has been written I guess is the main source of your > confusion. Let me provide a paper that explains this same concept in a way > that is easier to understand. I was confused by eq (3) that you mentioned > too. Here it is : > http://www.sau.ac.in/~vivek/softcomp/clustering%20PSO+K-means.pdf Ah,

Hello everybody, I Would like to get some help to plot together, Principal Components Analysis (PCA) and clusters. I am handling environmental data from 25 locations spread across 5 different ecosystems.When grouped into 5 clusters, locations from different ecosystems are arranged in the same group. So, I want to plot together PCA and Clusters, in a such way that locations belonging to the same

Dear R helpers, this is a question connex to the subject of reading large data-sets - I have a large db (MySQL) ~80'000 records for each year / 120 variates from which I extract relevant info to be treated in R (no problem) Now each record in my db represents an experimental unit; moreover I will get - in a few days- a set of separate files(one for each records....~80'000 files pro

I am following instructions online for cluster analysis using the mclust package, and keep getting errors. http://www.statmethods.net/advstats/cluster.html These are the instructions (there is no sample dataset unfortunately): # Model Based Clustering library(mclust) fit <- Mclust(mydata) plot(fit, mydata) # plot results print(fit) # display the best model This is what I did and the error I

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

Sorry, sorry.... of course levels(testf)[c(2,1,3)] will do the job My excuses to all Anne PS I will meditate the following saying "la parole est d'argent et le silence est d'or" BONNES FETES A TOUS SEASONAL GREETINGS ---------------------------------------------------- Anne Piotet Tel: +41 79 359 83 32 (mobile) Email: anne.piotet@m-td.com

Hello R helpers! I looked but did not find a table-lookup R-utility. I could use a loop to do the job (old FORTRAN/C habits die hard) but if I have a big table in which I have to search for the values corresponding to a vector, I end up logically with a double loop. Is there already such a utility? Otherwise, is there a way without loops? Thanks as always Anne

Is there an upgrate of the vcd library (visualisation of categorical data) for the latest R version? Trying to download it from CRAN I get URL /data/WWW/ftp/pub/R/bin/windows/contrib/r-release/vcd_0.1-3.4.zip was not found on this server. googling it, I found it for instance on http://www.sourcekeg.co.uk/cran/bin/windows/contrib/1.9/ but trying to install it gave me the message >