similar to: parallel::pvec FUN types differ when v is a list; code simplifications?

Hi, I understand ShortRead::FastqStreamer will read chunks in parallel depending on the value of ShortRead:::.set_omp_threads I see this discussed here: https://stat.ethz.ch/pipermail/bioc-devel/2013-May/004355.html and nowhere else. It probably should be documented in ShortRead. Possibly this has already changed for I am using still R 3.1.0. I thought I'd check. Oh, and, in my

[cross-posted on R-devel and Bioc-devel, since the functions from the parallel package discussed here are mirrored in the BiocGenerics package] Hi, I am currently running a lengthy simulation study (no details necessary) on a large multi-core system. The simulated data sets are stored in a long list and they are unevenly sized (hence, the computation times vary greatly between data sets), so

On 03/27/2014 02:13 AM, Ulrich Bodenhofer wrote: > I fully agree, Michael, that this would be a great thing to have! I have > often wondered why R and the standard packages are still sticking so > much to the old-style S3 flavor though S4 is part of standard R. I > acknowledge that backward compatibility is important, but, as far as I > got it, redefining a function or S3 generic as

Dear folks, Please welcome a newbie both to R and the mailing list :). I am currently working on a sequencing project, and heard about R as well as some of its packages for next gen sequencing, and decided to give it a try. Starting with ShortRead, I found a document (http://www.bioconductor.org/packages/2.5/bioc/vignettes/ShortRead/inst/doc/ShortRead_and_HilbertVis.pdf) which does mention

I fully agree, Michael, that this would be a great thing to have! I have often wondered why R and the standard packages are still sticking so much to the old-style S3 flavor though S4 is part of standard R. I acknowledge that backward compatibility is important, but, as far as I got it, redefining a function or S3 generic as an S4 generic should not harm existing functionality (if done

Hello, I see the following issue in R-devel since 'both' has become the default pkgType for binary platforms. update.packages() fails when you set options(repos). Looks like it is trying to download a tgz file from the src/contrib section of a repository (on a mac). To reproduce this you need to have an older version of AnnotationDbi installed, which I accomplished by faking it,

[cross-posted to R-devel and bioc-devel] Hi, I am trying to implement a 'sort' method in one of the CRAN packages I am maintaining ('apcluster'). I started with using setMethod("sort", ...) in my package, which worked fine. Since many users of my package are from the bioinformatics field, I want to ensure that my package works smoothly with Bioconductor. The problem

I'm trying to load an annotation file on a new R installation on a new machine (Win 7 x64, R 2.15.0) Loading this package fails; I've tried re-installing R and BioC from scratch, including all new packages etc, to no avail. Any ideas? > require("hugene10sttranscriptcluster.db") Loading required package: hugene10sttranscriptcluster.db Loading required package: AnnotationDbi

That might be worth thinking about generally, but it would still be nice to have the base generics pre-defined, so that people are not copy and pasting the definitions everywhere, hoping that they stay consistent. On Wed, Mar 26, 2014 at 6:13 AM, Gabriel Becker <gmbecker@ucdavis.edu>wrote: > Perhaps a patch to R such that generics don't clobber each-other's method > tables

Hello everybody, I've got a bit of a problem with parLapply that's left me scratching my head today. I've tried this in R 2.11 and the 23 bit Revolution R Enterprise and gotten the same result, OS in question is Windows XP, the package involved is the snow package. I've got a list of 20 rain/no rain (1/0) situations for these two stations i and j, all the items in this list look

Hi, I am new to bioconductor, trying to install KEGGSOAP package, but got warnings() when installing and error message when trying to load the package, can anyone suggest what went wrong? many thanks John > source("http://bioconductor.org/biocLite.R") Bioconductor version 2.11 (BiocInstaller 1.8.3), ?biocLite for help > biocLite("KEGGSOAP") BioC_mirror:

On 03/27/2014 06:31 PM, Herv? Pag?s wrote: > On 03/27/2014 02:13 AM, Ulrich Bodenhofer wrote: >> [...] >> >> For the time being, it seems I have three options: >> >> 1) not supplying the sort() function yet (it is not yet in the release, >> but only in my internal devel version) >> 2) including a dependency to BiocGenerics >> 3) leaving the

Hi, The acrobatics that as.data.frame.factor() is going thru in order to recognize a direct call don't play nice if as.data.frame() is an S4 generic: ??? df <- as.data.frame(factor(11:12)) ??? suppressPackageStartupMessages(library(BiocGenerics)) ??? isGeneric("as.data.frame") ??? # [1] TRUE ??? df <- as.data.frame(factor(11:12)) ??? # Warning message: ??? # In

> R.version.string [1] "R Under development (unstable) (2012-02-26 r58493)" In the recent addition "It is important if you export S4 methods that the corresponding generics are available: the requirementa on this are stricter as from R 2.15.0. You may for example need to import plot from graphics to make visible a function to be converted into its implicit generic. But it is

Hi, S4 method dispatch can be very slow. Would it be reasonable to cache the most recent dispatch, anticipating the next invocation will be on the same type? This would be very helpful in loops. fun0 <- function(x) sapply(x, paste, collapse="+") fun1 <- function(x) { paste <- selectMethod(paste, class(x[[1]])) sapply(x, paste,

Dear R-user, I am trying to use the function 'parLapply' from the 'snow' package which is supposed to work the same wys as 'lapply' but for a parallelized cluster of computers. The function I am trying to call in parallel is 'dudi.pca' (from the 'ade4' package) which performs principal component analyses. When I call this function on a list of

The runtime of parallel::parLapply depends on variables unrelated to the parLapply call. However, this is not clearly documented. Therefore I would like to suggest expanding the relevant documentation to explain this behaviour. Consider this example: parallel_demo <- function(random_values_count) { some_data <- runif(random_values_count) dummy_function <- function(x) { x }

Hi, Here are the issues I found: Typos ----- (a) Found: It a parallel version of ?evalq?, "is" missing. (b) Found: 'parLapplyLB', 'parSapplyLB' are load-balancing versions, intended for use when applying ?FUN? to 'parLapplyLB' has no 'FUN' arg (more on this below). (c) Found: 'clusterApply' calls 'fun' on the first

Given a vector : pvec=(p1,p2,.... p J) with sum(pvec)=1, all the elements are non-negative, that is, they are probabilities a matrix A ( N* J ), with the elements alpha(ij) are 0 or 1 I want to MAXIMIZE THE RESULT RESULT= product( i=1, to N [ sum ( alpha(ij)* pj , j =1,to J ) ] ) thus, I need to get pvec. how should I do ? for example

On Thu, Mar 15, 2018 at 3:39 AM, <FlorianSchwendinger at gmx.at> wrote: > Thank you for your answer! > I agree with you except for the 3 (Error) example and > I realize now I should have started with that in the explanation. > > From my point of view > parLapply(cl = clu, X = 1:2, fun = fun, c = 1) > shouldn't give an error. > > This could be easily avoided by