similar to: use of random and nested factors in lme

Dear all, I want to apply a weighted median on a huge dataset, and I remember a function from the package R.basic that could do this using an internal sorting algorithm qsort. This speeded things up quite a bit. Alas, I can't find that package anywhere anymore. There is a weighted.median function in the package limma too, but I didn't use that before. Anybody who knows what happened to

Dear all, I have a vector, and for each element I want to check whether it is equal to any element from another vector. I want a vector of logical values with the length of the first one as return. In R this would be : > x <- 1:10 > sapply(x,function(y){any(y==c("2","3","4"))}) [1] FALSE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE It works pretty

I think you are confusing package and function here. Plus some of the R Core packages, that you mention, contain functions that should probably be replaced by functions with better implementation from packages on CRAN. Best regards Morgan On Fri, 11 Oct 2019 15:22 Joris Meys, <jorismeys at gmail.com> wrote: > > > On Fri, Oct 11, 2019 at 3:55 PM Morgan Morgan <morgan.emailbox

Dear all, I have a vector of filenames which begins like this : X <- c("OrthoP1_DNA_str.aln", "OrthoP10_DNA_str.aln", "OrthoP100_DNA_str.aln", "OrthoP101_DNA_str.aln", "OrthoP102_DNA_str.aln", "OrthoP103_DNA_str.aln", "OrthoP104_DNA_str.aln", "OrthoP105_DNA_str.aln", "OrthoP106_DNA_str.aln",

Dear all, I want to apply a function to list elements, two by two. I hoped that combn would help me out, but I can't get it to work. A nested for-loop works, but seems highly inefficient when you have large lists. Is there a more efficient way of approaching this? # Make some toy data data(iris) test <- vector("list",3) for (i in 1:3){ x <- levels(iris$Species)[i] tmp

Dear all, I'm trying to optimize code and want to avoid for-loops as much as possible. I'm applying a calculation on subvectors from a big one, and I get the subvectors by using a vector of starting positions: x <- 1:10 pos <- c(1,4,7) n <- length(x) I try to do something like this : pos2 <- c(pos, n+1) out <- c() for(i in 1:n){ tmp <- x[pos2[i]:pos2[i+1]]

Dear All: stepping in late, but @Joris, if you would like to take 'from a single file' literally, have a look at: https://github.com/bpfaff/lp4rp (lp4rp: literate programming for R packages); Cheers, Bernhard ps: incidentally, within the noweb-file roxygen is employed. -----Urspr?ngliche Nachricht----- Von: R-devel [mailto:r-devel-bounces at r-project.org] Im Auftrag von Joris

I fully agree with Joris and Hadley on roxygen2. Additionally: I wrote and published my first package before roxygen (or roxygen2) was available. I found editing .Rd extremely terse (especially when code is updated). For example, the fact that there are no spaces allowed between } and { in \param{}{} has hurt my brain quite a few times, especially since R CMD check did not give any useful error

Thank you. I was just replying my own message with the same information. Sorry for not doing the research properly before filing. Cheers Joris On Sat, Mar 24, 2018 at 11:36 AM, Duncan Murdoch <murdoch.duncan at gmail.com> wrote: > On 24/03/2018 6:16 AM, Joris Meys wrote: > >> Dear all, >> >> while preparing some exercises I came across some highly surprising

Dear all, to my astonishment data.table cannot be installed on R 3.5 Windows. When checking the package page, the Windows binary is available for download. When checking the server however, I can't seem to find data.table. Also install.packages() says the package is only available in source form and may need compilation. Compiling using Rtools 35 is no problem. Is this merely an issue of

Dear all, I'm currently applying a mixed model approach to meta analysis using the package metafor. I use the "model.matrix()" function to create dummy variables. The option btt gives me the combined test for the dummies. Problem is, I don't know which indices I have to use, and can't really figure it out from the help file and the examples. I use following code : X <-

Dear all, I'm running a set of nonparametric MDS analyses, using a wrapper for isoMDS, on a 800x800 distance matrix. I noticed that setting the parameter k to larger numbers seriously increases the calculation time. Actually, with k=10 it calculates already longer than for k=2 and k=5 together. It's now calculating for 6 hours, and counting... There is quite a difference between the

And now I've seen I copied the wrong part of ?is.na > The default method for is.na applied to an atomic vector returns a logical vector of the same length as its argument x, containing TRUE for those elements marked NA or, for numeric or complex vectors, NaN, and FALSE otherwise. Key point being "atomic vector" here. On Sat, Jun 9, 2018 at 1:41 PM, Joris Meys <jorismeys at

Joris, the point is that 'z' is NOT used as a predictor in the model. Therefore it should not affect predictions. Also, I find it suspicious that the error only occurs when the response variable conitains missings and 'z' is unique (I have tested several other cases to confirm this). -Mark Op vr 16 mrt. 2018 om 13:03 schreef Joris Meys <jorismeys at gmail.com>: >

Dear all, as a follow-up to the question asked on R-package-devel (see link below): Someone sent a package to CRAN with a few problems. There's more things wrong with the submission, but one thing that really caught my eye was the following error: Warning message: running command '"C:/PROGRA~1/R/R-33~1.2/bin/x64/R" CMD BATCH --vanilla "testthat.R"

Dear all, I tried to read in a 3.8Gb RDS file on a computer with 16Gb available memory. To my astonishment, the memory footprint of R rises quickly to over 13Gb and the attempt ends with an error that says "cannot allocate vector of size 5.8Gb". I would expect that 3 times the memory would be enough to read in that file, but apparently I was wrong. I checked the memory.limit() and that

On 31/01/2018 6:33 AM, Joris Meys wrote: > 3. given your criticism, I'd like your opinion on where I can improve > the documentation of https://github.com/CenterForStatistics-UGent/pim. > I'm currently busy updating the help files for a next release on CRAN, > so your input is more than welcome. After this invitation I sent some private comments to Joris. I would say his

Dear all, to my astonishment data.table cannot be installed on R 3.5 Windows. When checking the package page, the Windows binary is available for download. When checking the server however, I can't seem to find data.table. Also install.packages() says the package is only available in source form and may need compilation. Compiling using Rtools 35 is no problem. Is this merely an issue of

Dear all, I asked this question on r-package-devel but Martin Maechler pointed out this was more suited on R-devel. So here it goes: per the manual, one should create and register both the S3 and a S4 method if one needs a method for an S4 class for a function using S3 dispatching. This is cumbersome, and not very optimal. I was wondering if there's a better way to do this. Currently I

Dear all, I initially ran into this problem while rebuilding a package dependent on nleqslv. I got the following error: Error in match.arg(global) : 'arg' must be of length 1 This didn't occur in previous versions of nleqslv, but did in the current one (2.4). I think I pinned the problem down to the following example: Take two functions: test <-