similar to: AW: AW: error-prone feature?

Hi All, while looking why the cclust(cclust) doesn't work for 1-dimensional data, I've found unpleasant behavior in semantics of R. Indeed: is.matrix(matrix(cbind(c(1,2,3,4)),ncol=2)[1:2,]) == TRUE but: is.matrix(matrix(c(1,2))[1:2,]) == FALSE kind regards, Valery A.Khamenya --------------------------------------------------------------------------- Bioinformatics

> Well, that is in all good texts on R, together with the > solution: drop=FALSE. See ?"[" for the on-line details. OK. Thank you a lot. Now patched cclust and clustIndex work fine for 1D case. BTW, why not to apply the "drop=F" to these functions? I guess other users need 1D case as well. kind regards, Valery A.Khamenya

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 all, is there any package in R capable of smooth approximation of CDF basing on given sample? (Thus, I am not speaking about ecdf) In particular, I expect very much that the approximation should subject to the property: f(x0)<=f(x1) for x0<x1, where x0 and x1 belong to range of the sample given. Polynomial approximation could be OK for me as well. P.S.

> On only ten points, what did you expect ? Even with 1000 > observations, estimating a density is difficult, and has > been the subject of a century of research. Kernel density > estimates are among the most successful. For your immediate > application, try plot(density(rnorm(10)), type="l"), etc. wait, you misunderstood me! I'd like to see 10 or 9 points with

Hi All, following expression: x <- sort(rnorm(10)); e <- ecdf(x); d <- numericDeriv(e(x),"x"); makes d far from approximation of one dimensional pdf. What's wrong then here? Kind regards. --------------------------------------------------------------------------- Valery A.Khamenya Bioinformatics Department BioVisioN AG, Hannover

Hi All, We'd like to use functions provided in R in our application. Our application is written in C/C++ and currently runs on win32, Linux and Mac. We'd be happy to attach the whole R ( i.e. not just transfer some function by hand). It is important that we deal with big amount of data, so "command line"-like invocations won't be very interesting. We'd

Hi All, We'd like to use functions provided in R in our application. Our application is written in C/C++ and currently runs on win32, Linux and Mac. We'd be happy to attach the whole R ( i.e. not just transfer some function by hand). It is important that we deal with big amount of data, so "command line"-like invocations won't be very interesting. We'd

Hi All is there "homals" package prepared for win32? kind regards, Valery A.Khamenya --------------------------------------------------------------------------- Bioinformatics Department BioVisioN AG, Hannover

> Almost any method of fitting a density estimate would work on > integrating (numerically) the result. it is a nice idea concerning the monotony property, which will be obtained automatically, but I am going to use results of approximation analytically > In particular, look at package polspline, where > p(old)logspline does the integration for you. thank you, I am going to

Dear Prof. Brian Ripley, first of all thank you for your answer, I do appreciate how do you manage to keep successfully all your activities and answer posts in this forum! > An empirical CDF is a step function: it does not have a > derivative at the jump points, and has a zero > derivative everywhere else. of course! Let me add few words concerning my simple motivation. 1.

> > 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

Hello llvmdev, that was one of the best things from the last week: >>>CS Inventory 2521 DCL >>>e-mail: inventory at cs.uiuc.edu >>>REMINDER: >>>Equipment items may not be scrapped, cannibalized or removed >>>from University premises for any purpose without prior approval from >>>the Business Office. For further information contact Rick

I would like to see an example of a data matrix for cclust and how to import it to cclust. In fact, i don't know how to give my imput for cclust program! i test this file 1 0.23 1.52 2 0.52 1.25 3 0.13 1.89 4 0.78 1.11 i do >library(cclust) >x<-scan("test.matrice.phyl") >cclust(x,2,method="kmeans") i have this error message: Error in sample(length(x),

Dear R users, When I run the following code, R crashes: require(cclust) x <- matrix(c(0,0,0,1.5,1,-1), ncol=2, byrow=TRUE) cclust(x, centers=x[2:3,], dist="manhattan", method="kmeans") While this works: cclust(x, centers=x[2:3,], dist="euclidean", method="kmeans") I'm posting this here because I am not sure if it is a bug. I've been searching

I have checked that section already. Sorry, I should have mentioned that. Memory limit increase does not work. Installtion of msvcrt.dll does not work either. Thank you. -----Original Message----- From: ripley at stats.ox.ac.uk [mailto:ripley at stats.ox.ac.uk] Sent: Wednesday, March 05, 2003 2:44 PM To: Igor Oleinik Cc: r-help at stat.math.ethz.ch Subject: Re: [R] problem with cclust[er]

Hi, I'm trying to debug a function I wrote to calculate the cindex for a hierarchical tree. For this it is useful to compare my calculations with those in output from the clustindex function, in the cclust library. There's no way, however, to have the cindex value for a given output of the cclust function, as a NA value is always returned. This happens almost surely because the cindex in

There is a new version of the 'CCLUST' package ,where i removed the extra command for the kmeans algorithm in the .R programm and also the comments about it in the .Rd help page. Now in the cclust library the kmeans algorithm can be applied only by using the cclust function. -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read

There is a new version of the 'CCLUST' package ,where i removed the extra command for the kmeans algorithm in the .R programm and also the comments about it in the .Rd help page. Now in the cclust library the kmeans algorithm can be applied only by using the cclust function. -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read

Hello, I am calling cclust function in cclust package repeatedly until some ceratain conditions for a cluster are met. Unfortunately, the system crashes on the second call (after debugging). # kmeans res1 is a well defined matrix cl <- cclust(res1, as.numeric(ncntrs), iter.max = 20, verbose = FALSE, dist="manhattan", method="kmeans") RGui has generated errors and will