similar to: error-prone feature?

> 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

> Nothing to do with me: you should report problems with > packages to the > maintainers, rather than R-help or a member of R-core. OK. I've sent a note about cclust patch to Evgenia Dimitriadou Thank you for your valueable comments. (No more reply needed in this thread) kind regards, Valery A.Khamenya ---------------------------------------------------------------------------

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

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

> 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.

Hi devels, i did not find at this page: http://developer.r-project.org/ideas.txt any ideas concerning incorporating documentation possibilities (say, Sweave-based) into R-scripts. Was it discussed already? (If discussed, then what is the decision/conclusion then?) thanks, Valery

Hi all, Situation: there is a function `f' already defined by someone and provided in package. `f' looks like that: f <- function() { x+1 } i.e. `f' is not closed i.r.t. term `x' now I have my own function `g', where I'd like to override variable `x' while calling `f': x <- "dummy gloabal value" g <- function() { x

Hi all, I took a look at last 2 months post in R-help maillist and surfed through the R-project.org . Unfortunately, I can't find some page with roadmap/statements about major changes coming in R-2.0 in comparison to R-1.9 Could anyone point me to the right URL? Thank you in advance. -- Valery

Hi all, I took a look at last 2 months post in R-help maillist and surfed through the R-project.org . Unfortunately, I can't find some page with roadmap/statements about major changes coming in R-2.0 in comparison to R-1.9 Could anyone point me to the right URL? Thank you in advance. -- Valery

Hi all, What is the right portable way to save graphics in batch mode? Remarks: 1. Problem is STFWed and RTFMed. In particular a short note about png() is found in R-FAQ. In fact, there were stated that png() is not reliable under Linux in batch mode. 2. savePlot under windows is quite convenient, but not supplied under Linux. 3. pdf() + postscript() < savePlot()

> See the 'Writing R Extensions' manual, specifically > Creating R Packages -> Writing package vignettes thank you, i saw this entry. However, this entry is rather about how to include documents (in particular Sweave-based) into a package. But I have meant smth else. Let me explain in example. Today I use emacs as environment for my R-sessions. I am quite happy to use

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),

Hello Chris, Thursday, January 8, 2004, 5:28:47 PM, you wrote: CL> Though I am mostly ignorant about leading edge distributed (i.e., loosely CL> coupled parallel) programming stuff, I _think_ that network latencies are CL> such that you really need the "big picture" of what a program is doing to CL> know how to profitably distribute and adapt the program to the environment

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