search for: distm

hi, I have a positive integer matrix like: test<-matrix(c(1,2,2,2,2,1,1,2,3),3) and based on the distant function I made like this: generateDistMat<-function (target) { n <- nrow(target) rn <- rownames(target) distM <- matrix(NA, n, n) diag(distM) <- 0 for (i in 1:(n - 1)) for (j in (i + 1):n) { distM[i, j] <- length(which(target[i, ] != target[j, ])) distM[j, i] <- distM[i...

Estimad en s, quería realizar una consulta Dado un data frame llamado aves, como puedo sustiuir las variables area, dist y distm por sus logaritmos en base10 y volver a crear un data frame? Muchas gracias Saludos Susana [[alternative HTML version deleted]]

...! I tried using "&&" or "&" , for isntance : subset1<-subset1[ (subset1$Class == 1)&& (subset1$Category == 1),] but it returns an empty data frame. Anyway, the main problem is Problem2: I have a second data frame - a square matrix (rownames == colnames), distm: distm<-read.table(file=f_simmatrix, sep = ","); what I want is select ONLY the columns and rows entries matching the above subset1: subset2<-distm[subset1$Dataset,subset1$Dataset] returns a matrix of correct size, but with incorrect entries (established by visual inspection)....

...> > URL shown. > > Hi Reid. > > Sorry to post off-list, but I'm not subscribed (reading through > gmane). > > The URL is fine. You can check that all svn URLs have that format > checking apache's access log. That is from my server: > > 81.41.168.35 - distm [20/Jul/2007:17:20:49 +0200] "PROPFIND > /dist/!svn/vcc/default HTTP/1.1" 207 392 > > 'dist' is the virtual directory that is managed by svn (when apache > receives a request for such directory, it delegates on the associated > module, in this case, mod_dav_svn.so....

...ot;gower", stand = FALSE, type = list(character=c("Place","Color"))) Then I clustered using DIANA dd.diana<-diana(dd.daisy) I am trying to cutreeDynamic {dynamicTreeCut} as follows cutreeDynamic(dd.diana, cutHeight = 2, minClusterSize = 1, method = "hybrid",+ distM = dd.daisy,+ deepSplit = (ifelse(method=="hybrid", 1, FALSE)),+ maxCoreScatter = NULL, minGap = NULL,+ maxAbsCoreScatter = NULL, minAbsGap = NULL,+ maxDistToLabel = cutHeight,+ respectSmallClusters = TRUE, + verbose = 2, indent = 0) But get the following error: Warning in if (class(dend...

...t;Taxa",cex.axis=1.5,cex.lab=2,cex.main=2.5,mgp=c(5,1.5,0),xaxt="n") text(1:(length(strat_name)), y= 0, adj=1, srt=45,labels=strat_name,xpd=TRUE, cex=1) #adds text to x axis giving the strat names abline(v=(1:ncol(PA))+.5,col="grey",lty=3) cl <- rep(1,nrow(PA)) cl[DistM$kill] <- 2 for (i in 1:nrow(PA)) segments(min(which(PA[i,]==1))-.5,i, max(which(PA[i,]==1))+.5,i,lwd=3,col=cl[i]) My problem lies with the x axis tick labels, I want my own labels added in so removed the one the plot function adds in normally. I use the text function as it allows me to fi...

Hi, I have produced a dendrogram of categorical data in R using the hclust function, although the input was a dissimilarity matrix produced in SAS, as I have defined my own distances. The dendrogram is fine and I can view and use this. However, I was wondering if there is a method by which I can find out the optimal place to place groups, rather than relying on my visual analysis? I don't

..., stand = FALSE, type = list(character=c("Place","Color"))) Then I clustered using DIANA dd.diana<-diana(dd.daisy) I am trying to cutreeDynamic {dynamicTreeCut} as follows cutreeDynamic(dd.diana, cutHeight = 2, minClusterSize = 1, method = "hybrid",+ distM = dd.daisy,+ deepSplit = (ifelse(method=="hybrid", 1, FALSE)),+ maxCoreScatter = NULL, minGap = NULL,+ maxAbsCoreScatter = NULL, minAbsGap = NULL,+ maxDistToLabel = cutHeight,+ respectSmallClusters = TRUE, + verbos...

Dear R-listers, Here, I would like to hearing helps from you. I have GPS data (multiple points in the geographic scale) in longitude/latitude. I intend to calculate distance (in kilometer) among such points and output the distance matrix in dist class. I have gotten some progress, but I still can not get final goal. Could please give me any directions/advice? This email cc. to Mr. Pierre, the

Hello, everybody, I hope somebody could help me with a dist() function. I have a data frame of size 2*4087 (col*row), where col corresponds to the treatment and rows are species, values are Hellinger distances, I should reconstruct a distance matrix with a dist() function. I know that "euclidean" method should be used. When I type: dist(dframe,"euclidean") it gives me a

Dear R-Help, I have a clustering problem with hclust that I hope someone can help me with. Consider the classic hclust example: hc <- hclust(dist(USArrests), "ave") plot(hc) I would like to cut the tree up in such a way so as to avoid small clusters, so that we get a minimum number of items in each cluster, and therefore avoid singletons. e.g. in this example, you can see