similar to: Split PVClust plot

Hi, I am new to clustering and was wondering why pvclust using "maximum" as distance measure nearly always results in p-values above 95%. I wrote an example programme which demonstrates this effect. I uploaded a PDF showing the results Here is the code which produces the PDF file: ------------------------------------------------------------------------------------- s <-

Hi all, I'm new to R and I'm struggling to decipher an error message. Briefly, I am trying to use the pvclust package to do hierarchical clustering of some CGH data. The data is from the Progenetix CGH database. It is arranged as a table where each column is a single case and each row is a single chromosome band. The value in each cell is either 0, 1, 2, or -1. Corresponding to no change,

Is there a way to assign color to nodes as with hclust/as.dendrogram/dendrapply when using pvclust? The problem is that as.dendrogram isn't working on the pvclust objects. library(pvclust) pvc <- pvclust(matrix, nboot=1000) plot(pvc) -- View this message in context: http://www.nabble.com/Node-colors-in-pvclust-tp24405329p24405329.html Sent from the R help mailing list archive at

Hi all I am writing to you with a question regarding the pvclust package. And yes, before the usual people produce their usual contact-the-package-maintainers line, ye, I tried that but the emails one can find on the web either bounce or are not responded to. Also, yes, this error has already been reported as a bug but been shot down as not reproducible

You should reply to the list, not just me, and even more because I cannot really help you!! My guess (but I don't know this package, and even less this function) is that pvclust() is expecting a matrix as the first argument. However, "cluster" is no data, it is a function. Why, I don't know. Take a closer look at ?pvclust, especially the "usage",

Hello all, I posted a question to this list last week and received no response. I am unsure if this means no-one knows the answer or if I posed the question badly. I'm going to assume I posed the question badly and try again. I am new to R so it is quite likely it's a very naive question, however if there is something blindingly obvious that I am missing or if there is another resource I

Hi there, I have been trying to use the "pvclust" package but have been having some difficulties. This is the first time I have used R so I am sure the mistake I am making is a basic one. The data I have is a distance matrix and I have been using the command; fit <- pvclust(cluster, nboot=1000, method.dist="euclidean") to try and perform hierarchical clustering with

I realize questions about packages should go to the package maintainer, but perhaps I have an old email address (suzuki3 at is.titech.ac.jp) Also I have both a general, and a specific, question. 1) General question: i've used pvclust before to assess significance of clusters and got reasonable results. However, on a new data set (see below) the results seem odd. I wonder if pvclust is a

Hi, if you look at the first image (Image1) you see that there are 2 main clusters 7 and 8 I wanted to use pvclust to calculate a p-value whether these clusters are due to chance or statistically significant. Unfortunately pvclust does not provide a p-value for the first brunch (7 and 8). So I added a row to my matrix which is very different to the rest of the data to create an additional

Hello, I am attempting to assign significance levels to a UPGMA cluster analysis as part my doctoral research. The pvclust function works well but doesn't include the similarity index I need (morisita's) as an option for computing a distance matrix. Morisita's is available in vegdist in the VEGAN library but I am having a hard time getting the vegdist function to "direct

Dear R-listers, Is anyone familiar with a package that would perform bootstrapping on species/site matrices for clustering ? So far I have been using the vegan package to generate trees (Bray-Curtis index), but I would like to associate a certainty to each node (similarly to a phylogenetic tree). If no package exist, I would know how to generate bootstrapped matrices of distance, but how

Hi all I am trying to perform the follwing: fit<-pvclust(wq, method.hclust="ward", method.dist="euclidean") but get a strange error message that I just cant figure out. Has anyone come across this? Any help would be most appricieated Error in hclust(distance, method = method.hclust) : NA/NaN/Inf in foreign function call (arg 11) In addition: Warning message: NAs

Hello there, Here's a question regarding p-values on clusters produced by hierarchical cluster analysis. A web search led me to the program pvclust to tackle this problem. But when I run the problem I get strange results. The 'AU' (approximately unbiased) p-values are very different from the 'BP' values (ordinary boot-strap) p-values. The AUs commonly are in the 80-100

Hi all, I have a problem with getting my code to do what I want! This is the code I have: create.means.one.size<-function(nsample,var,nboot){ mat.x<-matrix(0,nrow=nboot,ncol=nsample) for(i in 1:nboot){ mat.x[i,]<-sample(var,nsample,replace=T) } mean.mat<-rep(0,nboot) for(i in 1:nboot){ mean.mat[i]<-mean(mat.x[i,]) } sd.mean<-sd(mean.mat) return(mean.mat) } where

I wonder if someone could help me adjusting the following code to parallelized snow code: #Creating a data set (not needed to be parallel) n<-100 p<-100 x<-matrix(rnorm(n*p),p) y<-rnorm(n) # Bootstrapping nboot<-1000 alpha<-0.05 rhoboot <- array(0, dim=c(p,nboot)) bootranks <- array(0, dim=c(p,nboot)) bootsamples <- array( floor(runif(n*nboot)*n+1), dim=c(n,nboot)) for

Hi, Seeking suggestions to compute uncentered (pearson correlation) correlation efficiently. corr from stats library works on x and y columns. dist from amap library works on x and y rows. My data layout is slightly different such that row(i) of matrix x is compared to row(i) of matrix y. Thanks [[alternative HTML version deleted]]

Hi everybody, I'm trying to analyse a set of data with a non-normal response, 2 fixed effects and 1 nested random effect with strong heteroscedasticity in the model. I planned to use the function lmer : lmer(resp~var1*var2 + (1|rand)) and then use permutations based on the t-statistic given by lmer to get p-values. 1/ Is it a correct way to obtain p-values for my variables ? (see below)

I would like to get an hierarchical clustering tree with bootstrap values indicated on the nodes, as in pvclust. The problem is that I have only distance matrix instead of the raw data, required for pvclust. Is there a way to get it? fit1 <- hclust(dist) # an object of class '"dist" plot(fit1) # dendogram without p values library(pvclust) fit2 <- pvclust(raw.data,

Hi, I'm new to R (and statistics) and my boss has thrown me in the deep-end with the following task: We want to evaluate the impact that sampling size has on our ability to create a robust model, or evaluate how robust the model is to sample size for the purpose of cross-validation i.e. in our current project we have collected a series of independent data at 250 locations, from which

Hello, If you want to bootstrap a statistic, I suggest you use base package boot. You would need the data in a data.frame, see how you could do it. library(boot) bootMedianSE <- function(data, indices){ d <- data[indices, ] fit <- rq(crp ~ bmi + glucose, tau = 0.5, data = d) ypred <- predict(fit) y <- d$crp median(y - ypred)^2 } dat <-