similar to: MST dissimilarity

Dear R-helpers, could anybody explain me briefly what is the difference between eigenvectors returned by 'eigen' and 'svd' functions and how they are related? Thanks in advance Ondrej Mikula

Dear R-helpers, I need to fit natural cubic spline with specified number of knots. I expected 'splines' package will be helpful, but I am confused by its help. Is more detailed documentation available for it or could you recommend another R function? Best regards Ondrej Mikula

Dear r-helpers, I have a trouble with a package downloaded from sourceforge.net (namely the package 'kopls'). I installed it from the local zip file with the expected result > utils:::menuInstallLocal() package ?kopls? successfully unpacked and MD5 sums checked but when I tried to load it I obtained the following error message > require(kopls) Loading required package: kopls Failed

Dear r-helpers, I have a query regarding use of contrasts in MANOVA. summary(manova(model)) gives me only result of test for overall difference. Would you be so kind and give me a hint how to get the same test statistics (e.g.Pillai's) and P values for the predefined contrasts? Best regards Ondrej Mikula -- Institute of Animal Physiology and Genetics Academy of Sciences of the Czech

Dear R-helpers, I have a number of point configurations representing skull shapes, but some of them contain superfluous points. I want to write a loop in which each configuration is plotted and I am asked to write the numbers of points that are superfluous. However, I don't know how to introduce this interactive element. Would you give me an advice? Best regards Ond?ej Mikula -- Ond?ej

Dear List, This is Elaine, a postgraduate studying in bird distributions in East Asia. I want to calculate Simpson dissimilarity index, based on a presence/absence matrix of bird species in islands in East Asia. (matrix row: 36 islands/matrix column: species ID) (R package vegan to make NMDS and R package betapart) In most papers using vegan for NMDS and betapart for dissimilarity

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 accepted by "pam": A call

Dear All, I get some problems using the 'phylo' and dissimilarity functions in R. I converted an output from 'hclust' into an order of phylo so as to be able to use the 'consensus' function on it. Each time I submit the consensus codes, my computer hangs. When I tried to see what the contents of the object converted into order phylo is, I get the message

Hi all, I'm trying to perform a hierarchical clustering on some dissimilarity data that I have but the data matrix I have already contains the dissimilarity values. These values are calculated using a separate program. The dissimilarity matrix in complete with no missing values but the hclust, and agnes routines require it in the form produced by daisy or dist. Is there any of converting

Hi, I have a set of nodes and a dissimilarity matrix for them, as well as a csv file in which the diss matrix has been converted to [node_1, node_2, dissimilarity] format. I would like to visualize this as a graph in Euclidean space (that is, similar nodes clumped together in clusters), rather than the seriation visualization given by dissplot(). I am using Network WorkBench for my

Hello, I want to make a fuzzy classification from a dissimilarity matrix (calculated with daisy from package 'cluster'). I have tried to use fanny (package cluster) but I have the same problems than described in a previous message (http://tolstoy.newcastle.edu.au/R/help/05/05/4546.html) i.e. it always gives me two clusters in the results (even if k is different from 2) with the same

Dear all, My data includes almost three thousand people who rank ten categories into three variables. The simple example below is almost same except I have many missing values. x <- cbind( sample( LETTERS[1:10] , 3000 , replace = TRUE ) , sample( LETTERS[1:10] , 3000 , replace = TRUE ) , sample( LETTERS[1:10] , 3000 , replace = TRUE ) ) I try to figure

Is there a way of extracting mean distance or dissimilarity for a given strata from a 'dist' or 'dissimilarity' object, e.g. extract mean distances for each species in Anderson's iris data? data(iris) iris.dist<-dist(iris[,1:4]) then what? Mikkel Grum, PhD Genetic Diversity Scientist International Plant Genetic Resources Institute (IPGRI) Sub-Saharan Africa Group ***

Hi I have a large dataset, containing a wide range of binary variables. I would like first of all to compute a jaccard matrix, then do a PCA on this matrix, so that I finally can do a hierarchical clustering on the principal components. My problem is, that I don't know how to compute the jaccard dissimilarity matrix in R? Which package to use, and so on... Can anybody help me? Alternatively

I have 4 very large species lists and I would like to compare them. I have the following results from running Sorenson’s dissimilarity tests: Norfolk Fens compared to Suffolk Coastal Fens: QS=0.583961142689298 Norfolk Fens compared to Breckland Edge Fens: QS=0.714896020281379 Norfolk Fens compared to Other Fens:

Hi, According to daisy function from cluster documentation, it can compute dissimilarity when NA (missing) value(s) is present. http://stat.ethz.ch/R-manual/R-devel/library/cluster/html/daisy.html But why when I tried this code library(cluster) x <- c(1.115,NA,NA,0.971,NA) y <- c(NA,1.006,NA,NA,0.645) df <- as.data.frame(rbind(x,y)) daisy(df,metric="gower") It gave this

Dear r-helpers, I used lsfit to estimate regression coefficients from the matrices of predictors and predicted values (i.e multivariate multiple regression model). I wish, however, to compute rank-one matrices of predictor values from the predicted ones. How to do it in R? Could you suggest me appropriate function or formula in matrix notation? Best wishes Ondrej Mikula (Institue of Animal

Hi, I already have a dissimilarity matrix and I am submitting the results to the elbow.obj method to get an optimal number of clusters. Am I reading the below output correctly that I should have 17 clusters? code: top150 <- sampleset[1:150,] {cluster1 <- daisy(top150 , metric = c("gower") , stand = TRUE , type = list(symm

R users, Does anybody know how to convert a square matrix of distances (X^2 distances, actually) into an object of class "dissimilarity" so that the cluster package can used it? Thanks. Jord? L?pez Unv. Aut?noma de Barcelona -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send

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)