similar to: distance matrices

Dear List, Apologies for this off-topic post but it is R-related in the sense that I am trying to understand what R is telling me with the data to hand. ROC curves have recently been used to determine a dissimilarity threshold for identifying whether two samples are from the same "type" or not. Given the bashing that ROC curves get whenever anyone asks about them on this list (and

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

Hi, I am currently working on some data and feel that NMDS would return an excellent result. With my current data set however I have been experiencing some problems and cannot carry out metaMDS. I have tried with a few smaller data sets which I created for practice sake and this has worked fine. I think it is the set up of my data set that is causing me trouble. I have 18 columns and 18 rows,

Hello everybody. I am trying to cluster circular data (data points which are angles), thus i can not use the "dist" function in "mclust" to generate my distance matrix, I am using the function " Dij = 0.5*( 1 - cos(theta_i - theta_j)). The thing is "hclust" will not accept this distance matrix, i tried to put it in a data frame, but again i get an error message

If you submit these lines, you end up with variable "vare.dis". I want to export vare.dis to csv. Stuck I am. library(vegan,logical.return = TRUE) #return=true verifies package is available library(MASS,logical.return=TRUE) #return=true verifies package is available data(varespec) #varespec is an example data file in the vegan package vare.dis <- vegdist(varespec)

Hi, I'm trying to use a similarity matrix (triangular) as input for pam() or fanny() clustering algorithms. The problem is that this algorithms can only accept a dissimilarity matrix, normally generated by daisy(). However, daisy only accept 'data matrix or dataframe. Dissimilarities will be computed between the rows of x'. Is there any way to say to that your data are already a

Any reference to the appropriate documentation would be most appreciated. I am using the TSP module for clustering of HIV genetic sequences. The distances have already been computed and available as either upper-triangular or square, i.e.: a 1 2 3 b 4 5 c 6 d or a 0 1 2 3 b 1 0 4 5 c 2 4 0 6 d 3 5 6 0 The TSP modules takes in a "dist" object.

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 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)

Hi, I am trying to calculate a distance matrix on a binary data frame using dist.binary() {ade4}. This is the code I run and the error I get: > sjlc.dist <- dist.binary(as.data.frame(data), method=2) #D = (a+d) / (a+b+c+d) Error in if (any(df < 0)) stop("non negative value expected in df") : missing value where TRUE/FALSE needed I don't know if the problem are the

Hi there, I would like to find a more efficient way of permuting the rows and columns of a symmetrical matrix that represents ecological or actual distances between objects in space. The permutation is of the type used in a Mantel test. Specifically, the permutation has to accomplish something like this: Original matrix addresses: a11 a12 a13 a21 a22 a23 a31 a32 a33 Example

Dear All, I am in the somewhat unfortunate position of having to reproduce the results previously obtained from (non-metric?) MDS on a "kinship" matrix using Statistica. A kinship matrix measures affinity between groups, and has its maximum values on the diagonal. Apparently, starting with a nxn kinship matrix, all it was needed to do was to feed it to Statistica flagging that the

Hi All, I have a question which does not pertain directly to the use of R but comes from my use of R! I have data which can be described as 3-dimensional e.g. (x,y,z), with no negative component. The suggested way to analyze this data is via multivariate techniques or by calculating what amounts to a levene's test on the data and then an ANOVA on the three components if the first test is

H ello R-experts, I want to do ordination plots using vegan metaMDS. I have a where many cells have zero values. Data structure: X[1:10,1:14] Height.1 Height.2 Height.3 Height.4 Height.5 Height.6 Height.7 Height.8 Height.9 Height.10 Height.11 Height.12 Height.13 D30I1A 46 0 0 0 0 0 0 0 0 0 39 0 98 D30I1B

Hey Everyone! I wanted to carry out Hierarchical clustering using distance matrices i have calculated ( instead of euclidean distance etc.) I understand as.dist is the function for this, but the distances in the dendrogram i got by using the following script(1) were not the distances defined in my distance matrices. script: var<-read.table("the distance matrix i calculated",

Hi all, I've stumbled upon some memory limitations for the analysis that I want to run. I've a matrix of distances between 38000 objects. These distances were calculated outside of R. I want to cluster these objects. For smaller sets (egn=100) this is how I proceed: A<-matrix(scan(file, n=100*100),100,100, byrow=TRUE) ad<-as.dist(A)

Hello, In the documentation for agnes in the package 'cluster', it says that NAs are allowed, and sure enough it works for a small example like : > m <- matrix(c( 1, 1, 1, 2, 1, NA, 1, 1, 1, 2, 2, 2), nrow = 3, byrow = TRUE) > agnes(m) Call: agnes(x = m) Agglomerative coefficient: 0.1614168 Order of objects: [1] 1 2 3 Height (summary): Min. 1st Qu. Median Mean 3rd

Dear r-helpers! How can I integrate other distances (in the form of a dist object) into function metaMDS? The problem: metaMDS needs the original data.frame for the calculation and only the default distances of function vegdist are allowed. Any suggestions are greatly appreciated! Thank you, Kim -- Jetzt kostenlos herunterladen: Internet Explorer 8 und Mozilla Firefox 3.5 - sicherer, schneller

Hi all, I've been having trouble reading in distance matrices (values in lower triangle only) that have been created by other programs. They load with NA values in the upper triangle. Is there an option to read the matrices properly? Thanks, Muri ____________________________________________________________________________________ that gives answers, not web links.

Hello R-user community! I am running R 2.7.0 on a Power Book (Tiger). (I am still R and statistics beginner) Presently I try to run the function metaMDS (vegan) using an existing dissimilarity-matrix. As I would like to start with this matrix I thought I could just give the matrix using the x= -argument Test<-metaMDS(x=Dist.Gower) Fehler in inherits(comm, "dist") :