similar to: AW: AW: constructing specially ordered factor

Hi Dimitris, thank you for your reply, > does the following work in your data: > > levs <- unique.default(Names) > factor(Names, levs[order(unique.default(Weights))]) your solution is really shorter, but has two issues to be meant here: 1. "unique.default" is applied twice, what might be a bit expensive for strings. 2. your solution brings an implicit

> 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

> 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

> 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, 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.

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

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()

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, 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

Dear Christian, first of all thank you for your answer. I am going to parse through the pages you told me. Meanwhile I'd like to note that probably it is a good idea to put 2-3 lines of R-code demonstrating such a simple needs somnewhere in docs of `cluster' package. E.g. x<-rnorm(500) ... # output means we have rather 1 claster x<-c(rnorm(500), rnorm(500)+5)

Hi, I have a more complex example, but the problem boils down to this FOR-loop not filling in the res-matrix run_rows<-seq(0,1,0.05) run_cols<-seq(0.3,0.6,0.05) res<-matrix(NA,length(run_rows),length(run_cols)) for(i in run_rows) { for(j in run_cols) { res[i,j]=i+j #niether the above, nor res[[i,j]]=i+j work, why? } } Thank you, Serguei

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

> 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

Hello, I''m looking how (if) can I solve the following problem using HTB and iproute2: I need to assing the same bandwidth limit to every client, but the problem is that clients will be random - i.e. I know niether number of clients no IP or MAC addresses. If anybody knows FreeBSD''s ipfw2 - I''m looking for something like "ipfw pipe 150 config mask dst-ip 0xffffffff