similar to: How to Rank clusters

Dear R users, I am interested in clustering in R. In SAS we have some criteria for determining the number of clusters using the PROC CLUSTER procedure, which are "CCC" cubic clustering criterion (Sarl 1981), Psuedo F (PSF), and Psuedo T square (PST). My question is do thsese criterion exists in R, I tried to search and got one hit (BIC) in Mclust, which I am aware of, any input is

Hi all, once more about the old subj :-) My data has too much various distribution families and for every particular experiment I need just to decide whether the data is "quite homogeneous" or it has two or more clusters. I've revisited the following libraries: amap, clust, cclust, mclust, multiv, normix, survey. And I didn't find any ready-to-use general

Hi There, I'm working on some cluster analyses on a large data-set using hclust with Wards method and Manhattan (city block) distance measures. I've created dendrograms to illustrate the clustering criteria, but would like to create a plot to examine for the classic elbow criterion to use in determining the best number of clusters. Ideally I'd like to plot percent variance explained

SAS has something called the "cubic criterion" cutoff for finding the most appropriate number of clusters. Does R have anything that would replicate that? I've been searching the lists and can't seem to find anything that would point me in the right direction. Thank in advance, Philip Bermingham

> > It would be nice not only for me. > > I agree totally. If you belong to R-contributors group then thanks a lot in advance! > The problem is that you have to formalize what a cluster is, > and this is not a well defined notion. > It has different meanings in different applications. you are right if one follows the idea of full formalization of the notion it

Is there a command to get cluster criterion for the cluster methods? SAS has its criterion, but I prefer to do it in R. If there is not a command is there code to produce criteria to choose the number of clusters? Adrian Katschke Statistics Grad Student University of Nebraska-Lincoln [[alternative HTML version deleted]]

Hi, all, I?d like to do the clustering analysis in my dataset. The example data are as follows: Dataset 1: 500, 490, 486, 490, 491, 493, 480, 461, 504, 476, 434, 500, 470, 495, 3116, 3142, 12836, 3062, 3091, 3141, 3177, 3150, 3114, 3149; Dataset 2: 506, 473, 495, 494, 434, 459, 445, 475, 476, 128367, 470, 513, 466, 476,482, 1201, 469, 502; I had so many datasets like that. Basically, every

Hi, I'm actually trying to rank a set of candidate models with an information criterion (AICc, QIC, BIC). The problem I have is that I use mixed-effect cox regression only available with the package {coxme} (see the example below). #Model1 >spring.cox <- coxme (Surv(start, stop, Real_rand) ~ strata(Paired)+R4+R3+R2+(R3|Individual), spring) I've already found some explications in

As I want to generate code for the varaible declaration, at first, I thought AllocInstruction()could implement this. However, the name "alloc" seems to allocate memory from heap memory.and the local variable in function should stay at stack memory. In which way did llvm allocate memeory to AllocInstruction() ? Would the memory allocated by AllocInstruction() be recycled back? If not

Dear R users, I am trying to apply the analysis processed in a paper, on the data I'm working with. The data is: 80 patients for which I have survival data (time - days, and event - binary), and microarray expression data for 200 genes (predictor continuous variables). My data matrix "data.test" has ncol: 202 and nrow: 80. What I want to do is: - run recursive partitioning on

Dear R community, I'm a beginner with Cluster Analysis. I would like to know if there is a criterion to select the best set of clusters to do this analysis. Thanks in advance, [[alternative HTML version deleted]]

Hi, I am new to R and am using the ctree() function to do customer segmentation. I am using the following code to generate the tree: treedata$Response<-factor(treedata$Conversion) fit<-ctree(Response ~ .,controls=ctree_control(mincriterion=0.99,maxdepth=4),data=treedata) plot(fit) print(fit) The variable "Response" above equals 1 if the customer responded to an offering and

Hello everyone, I am Abhiroop, 3rd year engineering student from BITS Pilani, India. I was going through the idea list of Xapian Search Engine Library and the idea Learning to Rank interested me. I am not very well conversant with C++. However I have done a lot of development work in Java and Python. And i have done a project in C# too. Currently I am working on 2 projects: 1.Distributed Data

//Referring to the response posted many years ago, copied below, what is the specific criterium used for singularity of the gradient matrix? Is a Singular Value Decomposition used to determine the singular values? Is it the gradient matrix condition number or some other criterion for determining singularity? // //Glenn // / / /> What does the error 'singular gradient' mean

Hi Vijay, > I am Vijay Mahantesh SM from India. I am an open > source enthusiast and a big fan of computational mathematics and research. > I came across the idea list of mentioned in the link<http://trac.xapian.org/wiki/GSoCProjectIdeas> and > was fascinated to find projects of my passion. As per my understanding of > the project, this project requires a good

Hi all, I am running a series of experiments where after manipulating my data I run several clustering algorithms (agnes, diana and a clustering method of my own) on the data. I wanted to determine which clustering method did the best job, so therefore I had defined my own quality measure using two criteria: compactness of the data within the clusters themselves and the amount of seperation

I have not been able to find a way to do dense rank in R Here is an example of what I need rank() gives the following 5 rank 1 7 rank 2 7 rank 2 9 *rank 4* but I want 5 rank 1 7 rank 2 7 rank 2 9 *rank 3* * * thanks SS [[alternative HTML version deleted]]

>>>>> Serguei Sokol <sokol at insa-toulouse.fr> >>>>> on Mon, 22 Jan 2018 17:57:47 +0100 writes: > Le 22/01/2018 ? 17:40, Keith O'Hara a ?crit?: >> This behavior is noted in the qr documentation, no? >> >> rank - the rank of x as computed by the decomposition(*): always full rank in the LAPACK case. > For a

&lt;!--startrecall--&gt;&lt;img src=&quot;http://mail.skku.edu/mail/write/mail_recall.php?f_headindex=1215810-2003022633787-R-help@stat.math.ethz.ch&quot;&gt;&lt;!--endrecall--&gt; Hello! I have a question on rank function that i&#039;m working on now. Even though my English i not good, I hope you understand what i&#039;m asking for. It is a program that i

What is the easiest way to calculate a percent rank “by” an index key? Foe example, I have a dataset with 3 fields: Year, State, Income , I wish to calculate the rank, by year, by state. I also wish to calculate the “percent rank”, where I define percent rank as rank/n. (n is the number of numeric data points within each date-state grouping.) This is what I am