similar to: Predict Fanny Membership

Dear list, I have two dissimilarity matrices, one for a training data set which I then clustered using PAM. The second is a diss matrix for a validation data set (an independent field sample). I have been trying to use knn to distinguish distances between the validation data set and the 6 mediods of the training data defined by using PAM. I continue to get error messages in regards to either the

Full_Name: Guan-Hua Huang Version: 2.0.1 OS: Linux Submission from: (NULL) (140.113.114.123) I install the package cluster by using install.packages("cluster"). After install it, it runs fine for function clara, but it does not work for function fanny. I did the following things: library(cluster) set.seed(21) x <- rbind(cbind(rnorm(10, 0, 0.5), rnorm(10, 0, 0.5)),

Dear All, My R version: 2.0.1 ; OS using: Linux. I install the package cluster by using install.packages("cluster"). After install it, it runs fine for function clara, but it does not work for function fanny. I did the following things: library(cluster) set.seed(21) x <- rbind(cbind(rnorm(10, 0, 0.5), rnorm(10, 0, 0.5)),cbind(rnorm(15, 5, 0.5), rnorm(15, 5, 0.5)),cbind(rnorm(

Dear All, I am attempting to use the FANNY fuzzy clustering function in R (Kaufman & Rousseeuw, 1990), found in the "cluster" package. I have run into a variety of difficulties; the two most crucial difficulties are enumerated below. 1. Where is the 'm' parameter in FANNY? In _Finding Groups in Data: An Introduction to Cluster Analysis_ (1990) by Kaufman & Rousseeuw,

Hi, I am using fanny and I have estrange results. I am wondering if someone out there can help me understand why this happens. First of all in most of my tries, it gives me a result in which each object has equal membership in all clusters. I have read that that means "the clustering is entirely fuzzy". Looking at the graphics it is really difficult to understand how objects with so

I have "small" problem ... with the cluster library each time I try to use the "agnes","pam","fanny" functions with more than 20000 elements I get the following error: >Error in vector("double", length) : negative length vectors are not allowed >In addition: Warning message: >NAs introduced by coercion But with the clara

? stato filtrato un testo allegato il cui set di caratteri non era indicato... Nome: non disponibile Url: https://stat.ethz.ch/pipermail/r-help/attachments/20070329/5d6b2e57/attachment.pl

Hi all, I am wondering if anyone has written (or knows of) a function that will conduct a goodness-of-fit test for a gamma distribution. I am especially interested in test statistics have some asymptotic parametric distribution that is independent of sample size or values of fitted parameters (e.g., a chi-squared distribution with some fixed df), because I want to fit gamma distributions to

I've done a little digging, but I haven't been able to find an MPI or snow (or any other distributed processing) implementation of fanny (from cluster) or cmeans. Is anyone aware of some implementations or addons which I've missed? [I know of doi://10.1007/978-3-540-71351-7; but that's not for R] Don Armstrong [Currently not subscribed; M-F-T: set accordingly.] -- Certainly

Hello, Could someone please let me know the procedure for determining the 'best' solution with regards to the number of clusters using the Fanny algorithm for computing fuzzy clusters? The function requires a specification of the number of clusters a priori, but I am interested in determining what number of clusters would result in the ideal fit with the data. Any help/advice/pointers to

Hi, I'm trying to do PCA on a n by p wide matrix (n < p), and I'd like to get more principal components than there are rows. However, svd() only returns a V matrix of with n columns (instead of p) unless the argument nv=p is set (prcomp calls svd without setting it). Moreover, the eigenvalues returned are always min(n, p) instead of p, even if nv is set: > x <-

Hi, I'm trying to use Frank Harrell's Design:::ols function to do regression of y (numeric) on the interaction of two factors (x1 and x2), but Lapack throws an error: > library(Design) ... > load(url("http://www.csse.unimelb.edu.au/~gabraham/x")) > ols(y ~ x1 * x2, data=x) Error in chol2inv(fit$qr$qr) : 'size' cannot exceed nrow(x) = 20 > traceback()

Hi, I'd like to store large covariance matrices using Matrix classes. dsyMatrix seems like the right one, but I want to specify just the upper/lower triangle and diagonal and not have to instantiate a huge n^2 vector just for the sake of having half of it ignored: Dumb example: M <- new("dsyMatrix", uplo="U", x=rnorm(1e4), Dim=as.integer(c(100, 100))) diag(M) <- 1

Hi, I'm doing PCA on wide matrices and I don't understand why calling predict.prcomp on it throws an error: > x1 <- matrix(rnorm(100), 5, 20) > x2 <- matrix(rnorm(100), 5, 20) > p <- prcomp(x1) > predict(p, x2) Error in predict.prcomp(p, x2) : 'newdata' does not have the correct number of columns > dim(x2) [1] 5 20 > dim(p$rotation) [1] 20 5

Hi, I'm using Ubuntu Hardy i386, and the R 2.7.1 Ubuntu packages from CRAN. The CRAN r-base-dev package depends on refblas3-dev or atlas3-base-dev, but octave3.0-headers depends on libblas-dev. The two blas packages seem to conflict, so r-base-dev and octave3.0-headers can't be installed together. Is this a known issue? Thanks, Gad $ sudo apt-get install octave3.0-headers Reading

Hi, I'm using Ubuntu 9.10 amd64 with cran2deb packages (I know it's meant for Debian, it's worked fine for me for many months). Recently apt-get update has to started to complain: ... Hit http://debian.cran.r-project.org testing/ Release Get:1 http://debian.cran.r-project.org testing/ Packages [515kB] Fetched 1B in 3s (0B/s) W: Failed to fetch

Hi, I have two example packages, test1 and test2, where the only code in them is: setGeneric("predict", function(object, ...) standardGeneric("predict")) (get them from http://www.cs.mu.oz.au/~gabraham/test1.tar and http://www.cs.mu.oz.au/~gabraham/test2.tar) The difference between them is that first does not have a namespace, and loads fine. The second has a namespace

Hi R helpers, I'm fitting large number of single factor logistic regression models as a way to immediatly discard factor which are insignificant. Everything works fine expect that for some factors I get error message "Singular information matrix in lrm.fit" which breaks whole execution loop... how to make LRM not to throw this error and simply skip factors with singularity

Hi I am experiencing a problem with the random number generator. When I call any function that involve RNG such as "runif" or "sample" I get this error: .Random.seed is not an integer vector but of type 'double' I can't coerce the random seed and it's recommended not to alter it anyway. Can anybody help me? Thanks and kind regards, Piero Visconti, PhD

Hi, We're using stats4 for a logistic regression. The code is chdreg.logit2 <- glm(chd ~ age + sex, family = binomial) summary(chdreg.logit2) oddsratios <- coef(chdreg.logit2) exp(oddsratios) # Calculate model predicted values pred <- predict(chdreg.logit2,type="response") The glm part runs fine, and up to now so has the predict function. However, now we're