search for: diss

...BR(I)=1. 10 FLAG(I)=.TRUE. NCL=N C C Carry out an agglomeration - first create list of NNs C Note NN and DISNN are the nearest neighbour and its distance C TO THE RIGHT of I. C DO 30 I=1,N-1 DMIN=INF DO 20 J=I+1,N IND=IOFFST(N,I,J) IF (DISS(IND).GE.DMIN) GOTO 20 DMIN=DISS(IND) JM=J 20 CONTINUE NN(I)=JM DISNN(I)=DMIN 30 CONTINUE C 400 CONTINUE C Next, determine least diss. using list of NNs DMIN=INF DO 600 I=1,N-1 IF (.NOT.FLAG(I)) GOTO 600...

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, keep.data = !diss) And it responded... Error in agnes("E:D10.dist", diss = TRUE, metric = "euclidean", stand = FALSE, : x is not and cannot be con...

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...

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,]) S2<-sum(x[i,]^2) S3<-sum(x[j,]^2) sim2<-S1/(S2+S3-S1) diss2<-1-sim2 diss.all<-diss.all+diss2} it will cost a long time to finish this...

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 Sem...

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 <- read.csv2("data.txt",dec='.') > library(cluster) >...

...1] 68 NA 33 63 53 62 44 NA 20 69 NA 62 59 43 51 19 38 57 30 53 62 67 42 31 38 [26] 50 NA 69 67 38 NA 26 NA 52 39 45 42 58 79 92 53 NA 22 21 30 38 64 49 43 28 [51] 33 42 59 32 41 52 44 54 37 43 32 42 59 39 74 38 33 56 NA 52 38 46 42 29 58 [76] 54 62 32 53 39 28 34 24 44 46 27 38 > fanny(foo, k=2, diss=FALSE) Error in fanny(foo, k = 2, diss = FALSE) : No clustering performed, NA-values in the dissimilarity matrix. The help page says : In case of a matrix or dataframe, each row corresponds to an observation, and each column corresponds to a variable. All variables must b...

...ing I don't quite understand about the way it gets called and the way it returns values. That Fortran function gets called in hclust.R, like this: hclust <- function(d, method="complete") { <snip> n <- attr(d, "Size") if(is.null(n)) stop("invalid dissimilarities") labels <- attr(d, "Labels") len <- n*(n-1)/2 hcl <- .Fortran("hclust", n = as.integer(n), len = as.integer(len), method = as.integer(method), ia = integer(n), ib = integer(n), crit = double(n), mem...

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 matrix I have provide...

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. ---------------------------- Alessandro Semeria Models and Simulations Laboratory The Environment Research Center - Montecatini (Edison Group), Via Ciro Menotti 48, 480...

...emctl services: smb.service winbind.service My system is Centos 7. -- <<<<<<<<<<<<<<<<<<<------------------------------------------------------------------->>>>>>>>>>>>>>>>>>> < Disse-lhe Jesus: Eu sou o caminho, e a verdade e a vida; ninguém vem ao Pai, senão por mim > (João 14:6) Att. ♪ ♫ Luiz Guilherme N...

Dear All, Is there any existing code for plotting silhouette for kmeans clustering results? Many thanks! Linda [[alternative HTML version deleted]]

Dear list, I have two dissimilarity matrices, one for a training data set which I then clustered using PAM. The second is a diss matrix for a validation data set (an independent field sample). I have been trying to use knn to distinguish distances between the validation data set and the 6 mediods of the training data defined...

...ength(alpha.vec) -1 #MAXIMUM CLUSTERS NUM. ## --------------------------------- PAM - INDEX.G1 ------------------------------------------ res <- array(0,c(nc.max - nc.min +1,2)) res[,1] <- nc.min:nc.max clusters <- NULL for (nc in nc.min:nc.max) { cl <- pam(dist.mat,nc,diss=TRUE) res[nc-nc.min+1,2] <- G1 <- index.G1(as.matrix(alpha.vec),cl$cluster,d=dist.mat,centrotypes="medoids") clusters <- rbind(clusters, cl$cluster) } ############################################################################### I get the following error whenever I use...

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 = paste(&...

Hello, I would like to use my own dissimilarity matrix in a PAM clustering with method "pam" (cluster package) instead of a dissimilarity matrix created by daisy. I read data from a file containing the dissimilarity values using "read.csv". This creates a matrix (alternatively: an array or vector) which is not a...

...kely occurred in: > ### * chclust > > flush(stderr()); flush(stdout()) > > ### Name: chclust > ### Title: Constrained hierarchical clustering > ### Aliases: chclust plot.chclust bstick.chclust > ### Keywords: cluster hplot > > ### ** Examples > > data(RLGH) > diss <- dist(sqrt(RLGH$spec/100)^2) > clust <- chclust(diss) > bstick.chclust(clust, 10) Loading required package: vegan Warning in library(package, lib.loc = lib.loc, character.only = TRUE, logical.return = TRUE, : there is no package called 'vegan' Error in bstick.chclust(clust, 10...

Dear All , Since dissimilarity is one of quality measures in clustering , I'm trying to access to the sum of dissimilarity as a whole measure. But after running my data using CLARA I obtain : 1128 dissimilarities, summarized : Min. 1st Qu. Median Mean 3rd Qu. Max. 0.033155 0.934630 2.257000 2.941600...

...rs 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 export "clustering" and "data" to a new text file so I try > write.table(myclara$data,"cluster.dat") > write.table(myclara$clustering,"cluster.dat",append=TRUE) Vari...

...Centos 7. > > > > -- > > <<<<<<<<<<<<<<<<<<<----------------------------------------- > > -------------------------->>>>>>>>>>>>>>>>>>> > > > > < Disse-lhe Jesus: Eu sou o caminho, e a verdade e a vida; ninguém vem ao > > Pai, senão por mim > > > (João 14:6) > > > > Att. > >...