similar to: AW: constructing specially ordered factor

> Please follow the posting guide and do your homework before > posting, 1. my last homework in university was done a lot of years ago. 2. I always try to follow posting guide. > An object of the same type of 'x'. but if an element is equal to > one with a smaller index, it is removed. > > so the order is preserved, by definition. Here stated when

> 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

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

> 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, 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

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

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)

For ordered factor the natural contrast coding would be to parametrize by the succsessive differences between levels, which does not assume equal spacing of factor levels as does the polynomial contrasts (implicitly at least). This requires the contr.cum, which could be: contr.cum <- function (n, contrasts = TRUE) { if (is.numeric(n) && length(n) == 1) levs <- 1:n

> 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

Hi All, I have looked through the following thread: http://tolstoy.newcastle.edu.au/R/help/02b/4551.html and would like to ask after 1.5 year the same question: Are there any plans to involve multithread computations in R? Few word about my motivation: I use R on my OpenSSI cluster with 7 CPUs, so one could simply think of this cluster as of single computer with 7 CPU. My