similar to: bug with mapply() on an S4 object

The Bioconductor build for a package DirichletMultinomial on R Under development (unstable) (2013-02-26 r62077) -- "Unsuffered Consequences" at http://bioconductor.org/checkResults/devel/bioc-LATEST/DirichletMultinomial/george2-buildsrc.html shows * creating vignettes ... ERROR ... Error: processing vignette ?DirichletMultinomial.Rnw? failed with diagnostics: chunk 21 (label

Is there a missing value constant defined for R_xlen_t, cf. NA_INTEGER (== R_NaInt == INT_MIN) for int(eger)? If not, is it correct to assume that missing values should be taken care/tested for before coercing from int or double? Thank you, Henrik

With a recent SVN build (R Under development (unstable) (2013-07-10 r63264) -- "Unsuffered Consequences"), I'm having trouble installing the robustbase package. The bottom line is that I *think* it's a 32-bit-system problem, but I could easily be mistaken. robustbase is passing its package checks: http://cran.r-project.org/web/checks/check_results_robustbase.html ... but from

Hi, The function cummax() seems to have a bug. > x <- c(NA, 0) > storage.mode(x) <- "integer" > cummax(x) [1] NA 0 The correct result of this case should be NA NA. The mistake in [ https://github.com/wch/r-source/blob/trunk/src/main/cum.c#L130-L136] may be the reason. Best Regards, Dongcan -- Dongcan Jiang Team of Search Engine & Web Mining School of Electronic

Le 19/04/2018 ? 09:30, Tomas Kalibera a ?crit?: > On 04/19/2018 02:06 AM, Duncan Murdoch wrote: >> On 18/04/2018 5:08 PM, Tousey, Colton wrote: >>> Hello, >>> >>> I want to report a bug in R that is limiting my capabilities to >>> export a matrix with write.csv or write.table with over >>> 2,147,483,648 elements (C's int limit). I found

On 18/04/2018 5:08 PM, Tousey, Colton wrote: > Hello, > > I want to report a bug in R that is limiting my capabilities to export a matrix with write.csv or write.table with over 2,147,483,648 elements (C's int limit). I found this bug already reported about before: https://bugs.r-project.org/bugzilla/show_bug.cgi?id=17182. However, there appears to be no solution or fixes in upcoming

Inside of the anyNA() function, it will use the legacy any(is.na()) code if x is an OBJECT(). If x is a vector of POSIXct, it will be an OBJECT(), but it is also TYPEOF(x) == REALSXP. Therefore, it will skip the faster ITERATE_BY_REGION, which is typically 5x faster in my testing. Is the OBJECT() condition really necessary, or could it be moved after the switch() for the individual TYPEOF(x)

Hi, OK, if you try to explicitly make them generic, you are told that they are implicitly already generic: > setGeneric("is.finite", function(from, ...) standardGeneric("is.finite")) Error in setGeneric("is.finite", function(from, ...) standardGeneric("is.finite")) : "is.finite" is a primitive function; methods can be defined, but the

I'm trying to use Rprof() to identify bottlenecks and speed up a particullary slow section of code which reads in a portion of a tif file and compares each of the values to values of predictors used for model fitting. I've written up an example that anyone can run. Generally temp would be a section of a tif read into a data.frame and used later for other processing. The first portion

I believe the performance of isUnsorted() in sort.c could be improved by calling REAL() once (outside of the for loop), rather than calling it twice inside the loop. As an aside, it is implemented in the faster way in doSort() (sort.c line 401). The example below shows the performance improvement for a vectors of double of moving REAL() outside the for loop. # example as implemented in

Hi all, I found an unexpected behavior when I was trying to use the macro defined in "R_ext/Itermacros.h" to loop over an atomic vector. Here is a minimum example: C++ code ``` #include "R_ext/Itermacros.h" #define GET_REGION_BUFSIZE 2 //Redefine the macro since C++ is not happy with the implicit type conversion #define ITERATE_BY_REGION_PARTIAL(sx, px, idx, nb, etype,

I'm observing that base::sum(x, na.rm=FALSE) for typeof(x) == "double" is much more time consuming when there are missing values versus when there are not. I'm observing this on both Window and Linux, but it's quite surprising to me. Currently, my main suspect is settings in on how R was built. The second suspect is my brain. I hope that someone can clarify the below

On 10/25/19 11:01 AM, Tomas Kalibera wrote: > On 10/23/19 6:45 AM, Wang Jiefei wrote: >> Hi all, >> >> I found an unexpected behavior when I was trying to use the macro >> defined >> in "R_ext/Itermacros.h"? to loop over an atomic vector. Here is a >> minimum >> example: >> >> C++ code >> ``` >> #include

Dear all, According to CHANGES IN R 3.0.0: o diag() as used to generate a diagonal matrix has been re-written in C for speed and less memory usage. It now forces the result to be numeric in the case diag(x) since it is said to have 'zero off-diagonal entries'. diag(x) does not work for character vector in R 3.0.0 any more. For example, v <- c("a",

On Fri, 10 Feb 2023 23:38:55 -0600 Spencer Graves <spencer.graves at prodsyse.com> wrote: > I have a 4.54 GB file that I'm trying to read in chunks using > "scan(..., skip=__)". It works as expected for small values of > "skip" but goes into an infinite loop for "skip=1e11" and similar > large values of skip: I cannot even interrupt it; I

Hello, All: I have a 4.54 GB file that I'm trying to read in chunks using "scan(..., skip=__)". It works as expected for small values of "skip" but goes into an infinite loop for "skip=1e11" and similar large values of skip: I cannot even interrupt it; I must kill R. Below please find sessionInfo() with a toy example. My real problem is a large

Sorry for post a lot of things, for the first part of code, I copied my C++ iter macro by mistake(and you can see an explicit type casting). Here is the macro definition from R_exts/Itermacros.h #define ITERATE_BY_REGION_PARTIAL(sx, px, idx, nb, etype, vtype, \ strt, nfull, expr) do { \ * const** etype *px = DATAPTR_OR_NULL(sx); *

> Do you have R code (including set.seed(.) if relevant) to show on how to generate > the large square matrices you've mentioned in the beginning? So we get to some > reproducible benchmarks? Hi Martin, Here is the program I used. I only generate 2 random numbers and reuse them to make the benchmark run faster. Let me know if there is something I can do to help--alternate

In an earlier post, a person wanted to divide each of the rows of rawdata by the row vector sens so he did below but didn't like it and asked if there was a better solution. rawdata <- data.frame(rbind(c(1,2,2), c(4,5,6))) sens <- c(2,4,6) temp <- t(rawdata)/sens temp <- t(temp) print(temp) Gabor sent three other solutions and I understood 2 of them but not the

I get an error when using self-defined (not standard) functions with mapply with S4 objects from the raster package that I develop: "Error in as.character(sys.call(sys.parent())[[1]]) : cannot coerce type 'closure' to vector of type 'character'". Does anyone understand why? The problem is illustrated below. Thanks, Robert > # First a general example that works