similar to: Looking for internal of a function

Hello! I have noticed that mapply() drops names in R 2.3.1 as well as in r-devel. Here is a simple example: l <- list(a=1, b=2) k <- list(1) mapply(FUN="+", l, k) [1] 2 3 mapply(FUN="+", l, k, SIMPLIFY=FALSE) [[1]] [1] 2 [[2]] [1] 3 Help page does not indicate that this should happen. Argument USE.NAMES does not have any effect here as it used only in a bit special

--KsGdsel6WgEHnImy Content-Type: text/plain; charset=iso-8859-1; format=flowed Content-Disposition: inline Content-Transfer-Encoding: 8bit Hi, people. Wandering in R archives, and seeing the message attached below, I noticed that: mapply(rep,times=1:4, MoreArgs=42) still segfaults on R 2.2.0, and thought I should be a good citizen and report it, even if I do not have an actual problem

Hello everybody. I've been experimenting with mapply(). Does anyone else have problems with: R> mapply(rep,times=1:4, MoreArgs=42) (I get a seg fault). robin R> R.version _ platform powerpc-apple-darwin6.6 arch powerpc os darwin6.6 system powerpc, darwin6.6 status beta major 1 minor 8.0 year 2003 month 10 day 02 language R >

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

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

I have a data frame whose first colum contains the names of the variables and whose second colum contains the values to assign to them: : kkk <- data.frame(vars=c("var1", "var2", "var3"), vals=c(10, 20, 30), stringsAsFactors=F) If I do : assign(kkk$vars[1], kkk$vals[1]) it works : var1 [1] 10 However, if I try with mapply

Despite the spirited arguments of various R-core folks who feel that mle() doesn't need a "data" argument, and that users would be better off learning to deal with function closures, I am *still* trying to make such things work in a reasonably smooth fashion ... Is there a standard idiom for "merging" environments? i.e., suppose a function has an environment that I want

Dear expeRts, I am trying to wrap up a package "utilities" (for my internal use). After adding a function datNAtreat that uses mapply, R CMD check gives WARNINGs for "S3 generic/method consistency", "checking replacement functions" and?"checking foreign function calls", all of which are accompanied by the following error message: Error in .try_quietly

Dear list and Hadley, The new plyr package seems to provide a clean and consistent way to apply a function on several arguments. However, I don't understand why the following example does not work like the standard mapply, library(plyr) df <- data.frame(a=1:10 , b=1:10) foo1 <- function(a, b, cc=0, d=0){ a + b + cc + d } mdply(df, foo1, cc=1) # fine mdply(df, foo1, d=1) #

Hello, I'm attempting to create a data frame with correlations between every pair of variables in a data frame, so that I can then sort by the value of the correlation coefficient and see which pairs of variables are most strongly correlated. The sm2vec function in the corpcor library works very nicely as shown here: library(Hmisc) library(corpcor) # Create example data x1 = runif(50) x2 =

Hello again, Let say I have 2 matrices which equal number of columns but different number of rows like: Mat1 <- matrix(1:20, 4, 5) Mat2 <- matrix(1:25, 5, 5) Now for each column 1-to-5 I need to fetch the corresponding columns of these 2 matrices and add the corresponding elements (ignoring NA values if any). Therefore for the 1st column I need to do: (1+1), (2+2),...,(4+4), (NA+5) and

Dear all, please consider my following workbook: library(zoo) lis1 <- vector('list', length = 2) lis2 <- vector('list', length = 2) lis1[[1]] <- zooreg(rnorm(20), start = as.Date("2010-01-01"), frequency = 1) lis1[[2]] <- zooreg(rnorm(20), start = as.yearmon("2010-01-01"), frequency = 12) lis2[[1]] <- matrix(1:40, 20) lis2[[2]] <-

I have a general style question about R coding. Suppose I'm writing a function (foo1) that calls other functions (foo2, foo3, ...) which have complicated argument lists (e.g. optim(), plot()), _and_ I may be calling several different functions in the body of foo1. Since foo2 and foo3 have different sets of arguments, I can't just use "..." ; I did write some code a while ago

Hello all I would like to ask your help use mapply. I have a function called findCell that takes two arguments(x,sr) where x is a vector of size two (e.g x<-c(2,3) and sr is a matrix. I would like to call many times the findCell function (thus I need mapply) for different x inputs but always for the same sr. as x is a vector of size two (two cells) I want to pass inside inside the following

Dear users, I have a double for loop that does exactly what I want, but is quite slow. It is not so much with this simplified example, but IRL it is slow. Can anyone help me improve it? The data and code for foo_reg() are available at the end of the email; I preferred going directly into the problematic part. Here is the code (I tried to simplify it but I cannot do it too much or else it

Hello, I would like to use mapply to avoid using a loop but for some reason, I can't seem to get it to work. I've included copies of my code below. The first set of code uses a loop (and it works fine), and the second set of code attempts to use mapply but I get a "subscript out of bounds" error. Any guidance would be greatly appreciated. Xj, Yj, and Wj are also lists, and s2,

List: Thank you for the replies to my post yesterday. Gabor and Phil also gave useful replies on how to improve the function by relying on mapply rather than the explicit for loop. In general, I try and use the family of apply functions rather than the looping constructs such as for, while etc as a matter of practice. However, it seems the mapply function in this case is slower (in terms of CPU

R-helpers: I'm trying to build a package, but I'm a bit new to the whole S3/S4 methods concept. I'm trying to add a new definition of the zoo function "as.yearmon", but I'm getting the following error when it gets to this point during a package install: *** R CMD INSTALL STARStools * installing to library

Hi friends, there is methods() function to see the all available methods for a particular function, for example: > head(methods("print")) [1] "print.acf" "print.anova" "print.aov" "print.aovlist" "print.ar" "print.Arima" In this list, there are some functions which are asterisked like print.acf().

Hello - I am wanting to create some Cox PH models with coxph (in package survival) using different datasets. The code below illustrates my current approach and problem with completing this. ### BEGIN R SAMPLE CODE ############################## library(survival) #Define a function to make test data makeTestDF <- function(n) { times <- sample(1:200, n, replace = TRUE) event