search for: asymm

Hi! I am trying to calculate a dissimilarity matrix using daisy. The matrix vectver is binary as i test with: > levels(as.factor(vectver)) [1] "0" "1" But the call to daisy gives me the following error message.: > dfl1 <- daisy(vectver, type = list(asymm = c(1:length(vectver[,1])))) Error in daisy(vectver, type = list(asymm = c(1:length(vectver[, 1])))) : at least one binary variable has more than 2 levels. and the call to mona that. levels(as.factor(vectver)) [1] "0" "1" > r<-mona(vectver) Error in mona(vectver) : All...

Dear R list users, the singluar value decomposition of a symmetric matrix M is UDV^(T), where U = V. La.svd(M) gives as output three elements: the diagonal of D and the two orthogonal matrices u and vt (which is already the transpose of v). I noticed that the transpose of vt is not exactly u. Why is that? thank you for your attention and your help Stefano AVVISO IMPORTANTE: Questo messaggio di

...lw) everparti=as.factor(everparti) endozoo=as.factor(endozoo) epizoo=as.factor(epizoo) aquat=as.factor(aquat) wind=as.factor(wind) detach(basetraits) #dissimilarities calculation library(cluster) daisybase<-daisy(basetraits,stand=FALSE,type=list(numeric=c(1,7),ordrati o=c(8,11),symm=c(12,14),asymm=c(15,40))) [[alternative HTML version deleted]]

...why. Last time I unwittingly sent my message with HTML formatting. This time I'm travelling and using an e-mail system that I am unfamiliar with. As far as I can see, I am not using HTML. Best wishes, Mikkel > library(cluster) > data(flower) > dfl1 <- daisy(flower, type = list(asymm = 3)) > hdfl1<-hclust(dfl1,method="average") > plot(hdfl1) > ddfl1<-as.dendrogram(hdfl1) > cdfl1<-cut(ddfl1,h=0.5) > cdfl1<-cut(ddfl1,h=0.4) > cdfl1<-cut(ddfl1,h=0.31312) > cdfl1<-cut(ddfl1,h=0.31311) Error: more elements supplied than there are to r...

...e below when both use method="average"?? I'm not able to reproduce the problem with other datasets. ereck<-read.table("Ereck.txt",header=TRUE,sep="\t") emol<-subset(ereck,select=c(11:18,20:32)) library(cluster) library(mva) daisemol<-daisy(emol,type=list(asymm=c(1:21))) layout(matrix(c(1,1,2,2),2,2,byrow=TRUE)) hclustmol<-hclust(daisemol,method="average") plot(hclustmol,cex=0.6) agnesmol<-agnes(daisemol,method="average") pltree(agnesmol,cex=0.6) coagnes<-cophenetic(agnesmol) cor(coagnes,daisemol) cohclust<-cophenetic(hcl...

Any idea why clusters five and six fall off the scale in the following? library(cluster) library(maptree) data(flower) dfl2<-daisy(flower,type=list(asymm=c(1,3),ordratio=7)) hdfl2<-hclust(dfl2) prune.dfl2<-prune.clust(hdfl2,k=6) plot(prune.dfl2) Of course the following will display all six clusters, but without a scale: draw.tree(prune.dfl2) Best wishes, Mikkel Mikkel Grum International Plant Genetic Resources Institute (IPGRI) Sub-Sahar...

I am currently using the function daisy in package cluster to create a dissimilarity matrix because my multivariate dataset contain missing data and variables of various types including factors, symmetric and asymmetric binary and quantitative. This is a step prior to using pco within ecodist. There is a warning which comes twice ">NAs introduced by coercion" I've used the option: type = list(asymm = c(4:30,32:42), symm= c(31,43)) to indicate which are binary variables and their s...

...SE,type=list(nominal=c(1,2,3), ordinal=c(4,5))) >summary(dm) 38434528 dissimilarities, summarized :    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 0.00000 0.25000 0.35000 0.36599 0.50000 1.00000 Metric :  mixed ;  Types = I, I, I, I, I Number of objects : 8768   >dfl=daisy(flower,type=list(asymm=1:3,nominal=4,ordinal=5:6,interval=7:8)) > summary(dfl) 153 dissimilarities, summarized :    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 0.15915 0.43576 0.53408 0.53473 0.62908 0.89099 Metric :  mixed ;  Types = A, A, A, N, O, O, I, I Number of objects : 18     Penny   [[alternativ...

Hi List, I am looking for a variable selection procedure with a forward-backward selection method. Firstly, it is meant to work with the cophenetic correlation coefficient (CPCC) and intended to find the variable combination with the highest cophenetic correlation. Secondly, it is aimed at Gower metric with wards method (though this could be easily extended) aimed at categorical data. What I

Hi, First I should note I am relatively new to R so I would appreciate answers that take this into account. I am trying to perform an MDS ordination using the function ?metaMDS? of the ?vegan? package. I want to ordinate species according to a set of functional traits. ?Species? here refers to ?sites? in traditional vegetation analyses while ?traits? here correspond to ?species? in such