search for: distmat

Hi all, I wanted to run the hclust (or any other clustering algorithm) on a distance matrix. I have formed the distance matrix as: distmat: a b c d e a 0.00 0.96 1.60 1.60 1.68 b 0.96 0.00 0.96 1.80 2.64 c 1.60 0.96 0.00 0.84 1.80 d 1.60 1.80 0.84 0.00 0.96 e 1.68 2.64 1.80 0.96 0.00 Now, I would like to run a clustering a...

...alculate overlapping U-diametric clusters of a given radius. (Again, I apologize this looks so much like C.) ## Returns a list of all U-diametric clusters of a given radius ## Give an R distance matrix ## Clusters may overlap. Clusters may be identical (redundant) getUDClusters <-function(distmat,radius){ mem <- list() nItems <- dim(distmat)[1] for ( i in 1:nItems ){ mem[[i]] <- c(i) } for ( m in 1:nItems ){ for ( n in 1:nItems ){ if (m != n & (distmat[m,n] <= radius)){ ##item is within radius, so add to collection m mem[[m]] <...

Dear All, I have a dissimilarity matrix which I happily convert to a distance object by running: X <- as.dist(Y) and I can happily now run either hclust(X) or agnes(X). So that the various bits of output are labelled correctly I would dearly like to be able to give names to the columns and rows of X, as would happen if I ran: mydata<-read.table("clipboard",header=T)

...uot;http://epi.whoi.edu/ipython/results/mdistefano/pg_site1.csv",header=T) dd <- mat[!is.na(mat$idcode) & !is.na(mat$temp) & !is.na(mat$sal) & !is.na(mat$count) & !is.na(mat$count) & !is.na(mat$subs),] distmat<-vegdist(dd) clusa<-hclust(distmat,"average") print(clusa) Call: hclust(d = distmat, method = "average") Cluster method : average Distance : bray Number of objects: 8036 print(dend1 <- as.dendrogram(clusa)) 'dendrogram' with 2 branches and 8...

Hi everyone - I know that R is capable of clustering using the k-means algorithm, but can R do k-means++ clustering as well? Thanks, -- Dr. Ferebee Tunno Assistant Professor Department of Mathematics and Statistics Arkansas State University P.O. Box 70 State University, AR. 72467 ftunno@astate.edu (870) 329-7710 [[alternative HTML version deleted]]

Hi all, I have a dataframe of rownames that I would like to extract from a larger matrix to form a new matrix. I have tried to use subset, in this manner x<-subset(largematrix, rownames$names=largematrix$rownames) where largematrix is the larger matrix and rownames$names is the dataframe with the row names of the rows I want to extract from the larger matrix. Of course, I get error

...constraining variable. I've read the documentation, but setting first=FALSE or using by="axis" doesn't seem to be helping. capscale seems to be fine, I receive output for more than one constrained axis. What am I doing wrong? capscale.Nrem.results<-capscale(as.dist(qiime.data$distmat)~ N+rem+N*rem+Condition(dateFac), factor.frame) capscale.Nrem.results Inertia Proportion Rank Total 1.454538 Real Total 1.459802 1.000000 Conditional 0.117117 0.080228 1 Constrained 0.386228 0.264576 3 Unconstrained 0.956457 0.655197 22 Imaginar...

...ps ecological relationships become less relevant that stochastic processes above a certain threshold. The problem is that I can't find a way to do it. If I replace values in either or both of the distance matrices with NA, mantel.rtest (ade4 package) gives the following error: Error in if (any(distmat < tol)) warning("Zero distance(s)") : missing value where TRUE/FALSE needed Here's a trivial example that tries to exclude elements of the first matrix that equal 11: library(ade4) a <- matrix(data = 1:36, nrow = 6) b <- matrix(data = 1:36, nrow = 6) a[a==11] <- NA mante...

Dear useRs, I have a very basic question. I have a distance matrix and i skipped the upper part of it deliberately. The distance matrix is 1000*1000. Then i used "min" command to extract the lowest value from that matrix. Now i want to know what is the location of that lowest element? More precisely, the row and column number of that lowest element. Thanks in advance elisa

Hi all, Thanks for your help in generating the matrix of distance vs correlation. I did it using plot(as.vector(as.matrix(cormat)), as.vector(as.matrix(distmat))) Now I want to quantitate the same. May be on linear regression or some other statistical functions. I have tried using linmod for linear regression. But as I have two matrices in the form of the dataframes, I'm wondering if it is the right way to do it in this? Or are there even better opti...