search for: subcluster

Hello, I have a specific problem, I have a large dataframe, and after clustering I want to select certain colums, the elements of a subcluster. My dataframe looks like this : > colnames(data) [1] "101KF4319097339" "102KF4319101170" "103KF4319047549" "104KF4319046389" [5] "105KF4319013260" "106KF4319025582" "107KF4319108763" "108KF4319047040" [9] &qu...

...ory cannot be "read". and then i have to close R. what is the problem and how can i solve it? thanks in advance Avi my code # frailtypack library(frailtypack) cgd.ag <- read.csv("C:/rfiles/RE/cgd.csv") cgd.nfm <-frailtyPenal(Surv(TStart, TStop, Status)~cluster(Center)+subcluster(ID) Treatment,data=cgd.ag,Frailty=TRUE,n.knots=8,kappa1=50000, cross.validation=TRUE,recurrentAG=TRUE) -- View this message in context: http://r.789695.n4.nabble.com/memory-problem-tp2330510p2330510.html Sent from the R help mailing list archive at...

Good afternoon, I ran heatmap and hclust on the same matrix x (strictly, I ran heatmap(x), and hclust(dist(t(x))), and realized that the two dendrograms were slightly different, in that the left-right arrangement of one pair of subclusters (columns) was reversed in the two functions (but all individual columns were grouped correctly). Looking through the code for heatmap as a most definite nonexpert, it seems to me that hclust is also invoked by heatmap. > heatmap function (x, Rowv = NULL, Colv = if (symm) "Rowv" else...

Hi, all. I'm trying to cluster 12,500 objects using hclust from package mva. The distance matrix takes up nearly 600 MB. The distance matrix also needs to be copied when being passed to the fortran routine that actually does the clustering (it's modified during the clustering), so that's 1200 MB. I'm actually on a machine with 2.5 GB of memory (and nothing else running), so I

...therefore 14000 labels, michael> it's going to be difficult to display your tree in michael> ANY software, including the expensive commercial products. not showing the labels and using identify.hclust() and the command line to extract the indices of observations in clusters (and subclusters) and visualize them in other, non-dendrogram plots, might well be feasible. michael> Let me know how you get on michael> Thanks michael> Mick michael> -----Original Message----- michael> From: wmak at brandeisedu [mailto:wmak at brandeis.edu] michael>...

...(t1,t2,event)~cluster(group)+var1+slope(var1), correlation=TRUE,data=dataAdditive,n.knots=8,kappa1=10000) -- NESTED FRAILTY MODELS for hierarchically clustered data (with 2 levels of clustering) by including two random effects: fitnested<-frailtyPenal(Surv(t1,t2,event)~cluster(group)+subcluster(subgroup), data=dataNested,n.knots=8,kappa1=50000) -- JOINT FRAILTY MODELS in the context of joint modelling of recurrent events with terminal event: fitJoint<-frailtyPenal(Surv(time.entry,time.end,status)~cluster(id)+ terminal(status.terminal),formula.terminalEvent=~status.termina...

...considered standard? The "approximately unbiased" feature of pvclust compared to regular boostrapping seems attractive. 2) Specific question: the odd result I am getting concerns a tree with a very clear division into two very distinct top level clusters. However on this data set the subclusters with confidence appear low down in the tree, and the very top most division gets zero significance. I'm suspicious of this given the rather clear top-level clade structure in this data set with lots of examples and not many NA's, i.e. pretty vanilla data. Also, in a related data set there...

...(t1,t2,event)~cluster(group)+var1+slope(var1), correlation=TRUE,data=dataAdditive,n.knots=8,kappa1=10000) -- NESTED FRAILTY MODELS for hierarchically clustered data (with 2 levels of clustering) by including two random effects: fitnested<-frailtyPenal(Surv(t1,t2,event)~cluster(group)+subcluster(subgroup), data=dataNested,n.knots=8,kappa1=50000) -- JOINT FRAILTY MODELS in the context of joint modelling of recurrent events with terminal event: fitJoint<-frailtyPenal(Surv(time.entry,time.end,status)~cluster(id)+ terminal(status.terminal),formula.terminalEvent=~status.termina...

Hello, I have a cohort with approx 1,200 patients at the ages of 30-65 that had their first myocardial infarction during 1992: · They were in a follow up until 2005. · About 400 of them died during this period of time (right censored) · Each one of them had up to 4 mi recurrent events. I am using the semi-parametric model in order to assess the relationship of

...or further analysis. The data is too large for the use of convenient tools such as identify.clust (it selects the specific group of interest on a graph), so alternatively I have saved the plot as a large image file so that it can be printed or viewed piecemeal. My problem is this: when I find the subclusters that I need to analyze I have no way to reference that specific component of the dendrogram in order to extract it: smallDend <- dend[[i]][[j]][[k]] where i,j,k refer to levels of the dendrogram (it's "address") I would like to print the "address" of the smaller dendr...

Dear R user, I am a biochemist/bioinformatician, at the moment working on protein clusterings by conformation similarity. I only started seriously working with R about a couple of months ago. I have been able so far to read my way through tutorials and set-up my hierarchical clusterings. My problem is that I cannot find a way to obtain information on the rooting of specific nodes, i.e. of