similar to: Problem with the special argument '...' within a function

Hi R-users, I have a function (myfun) that I want to apply to the rows of a matrix. Basically, "myfun" takes the values from the matrix ("exp.des"), which represent the different combinations of my experimental design, and pass them as arguments to some other functions (fun1 and fun2). As I want to replicate the results of fun1 and fun2 a certain number of time (e.g. 5), I

Dear list, I have two functions created in the same environment, fun1 and fun2. fun2 is called by fun1, but fun2 should use an object which is created in fun1 fun1 <- function(x) { ifelse(somecondition, bb <- "o", bb <- "*") ## mymatrix is created, then myresult <- apply(mymatrix, 1, fun2) } fun2 <- function(idx) { if (bb == "o) { #

Hello I need some help in figuring Bravington’s debugger out. Ok I have 2 functions, fun1 and fun2 saved in a ASCII file say filename is funs. Fun1 has a loop which calls fun2, fun2 has a loop which fails and I need to find out the value of the variables of the fun2 and fun1 loops at the specific iteration that fails. Both fun1 and fun2 loops will iterate thousands of times so line by line debug

Dear list!   I would like to write a function to transform matrix, which is input argument of a written function. It is easy with new matrix (see below), but my idea is to transform input argument (matrix) of function without any additional matrixes. Here is an example: fun1 <- function(xy) { xy <- cbind(xy[,1], xy[,2], xy[,1] + xy[,2]) return(xy) }   df1 <- matrix(c(1,2,3,1,2,3), ncol =

Hi R users: I'm trying to make a function where two of the parameters are functions, but I don't know how to put each set of parameters for each function. What am I missing? I try this code: f2<-function(n=2,nsim=100,fun1=rnorm,par1=list(),fun2=rnorm,par2=list()){ force(fun1) force(fun2) force(n) p1<-unlist(par1) p2<-unlist(par2) force(p1) force(p2)

Dear list, I know I have seen this discussed before but I haven't been successful in searching for "ellipsis", "dots", "..." in the archives. I would like to filter "..." arguments according to their name, and dispatch them to two sub-functions, say fun1 and fun2. I looked at lm() but it seemed more complicated than I need as it modifies the calling

Dear Friends, Let's assume there are three parameters that were passed into fun1. In fun1, we need to modify one para but the remains need to be untouched. And then all parameters were passed into fun2. However, I have failed to achieve it. Please see the following code. ########################################## fun1 <-function(x, y, z=10) {x+y+z;} fun2 <-function(aa, ...) {

Hello, I'm building a package. My code is stored in foo.R. This code has two functions FUN1 and FUN2. FUN1 calls FUN2. FUN1 is listed in export() under the package NAMESPACE but NOT FUN2. After building the package when I call FUN1 is giving me an error that cannot find FUN2. I solved this by adding FUN2 in the export() NAMESPACE. However, what is puzzling me is that I have other examples

Dear list! ? I have question of?'correct function formation'. Which function (fun1 or fun2; see below) is written more correctly? Using ''structure'' as output or creating empty ''data.frame'' and then transform it as output? (fun1 and fun1 is just for illustration). ? Thanks a lot, OV ? code: input <- data.frame(x1 = rnorm(20), x2 = rnorm(20), x3 =

Dear R-users, i have to integrate the following function `fun1` <- function (a, l1, l2) { exp(log(l1) * (a - 1) - l2 * lgamma(a)) } but if l1 is large, i get the "non-finite function value" error, so my idea is to rescale with exp(-l1) `fun2` <- function (a, l1, l2) { exp(log(l1) * (a - 1) - l2 * lgamma(a) - l1) } but it seems this doesn't solve the problem, when

Hi, I have a vector a=c(NA, 3, 4, 4, NA, NA, 3) and I would like to use is.na(a) function to get a vector like this: wy=(1,2,2,2,1,1,2) - you know, this vector create 1 or 2 depends on value in vector "a" This is my short code but something is wrong and I don't know what... for (i in 1:7){ a=c( NA, 3, 4, 4, NA, NA, 3) fun1=function(x){ x=1 print(x) } fun2=function(x){

I'm noticing some interesting behavior in `match.call` in some corner-ish cases that arise when you try to use `match.call` to match a "grandparent" function and there are dots involved: fun0 <- function(a, ...) fun1(...) fun1 <- function(b, ...) fun2() fun2 <- function() match.call( fun1, sys.call(sys.parent()), expand.dots=FALSE,

> On Feb 25, 2015, at 5:35 PM, Benjamin Tyner <btyner at gmail.com> wrote: > > Actually, it depends on the number of cores: Under current semantics, yes. Each 'stream' of function calls is lazily capturing the last value of `i` on that core. Under Luke's proposed semantics (IIUC), the result would be the same (2,4,6,8) for both parallel and serial execution. This is

I have stumbled across some behaviour in R that I really can't place, and that makes coding a bit tricky. I know that I can work around it when explicitly checking for missing arguments, but still... I have two functions. I have a first function based on paste ? ? fun1 <- function(x,y){ ? ? ? print(missing(y)) ? ? ? paste(x,'X',sep=y) ? ? } If I try this function without

Hello! Maybe "frame" is not the right term in this context. I explain my problem by example code: fun2 <- function(objName, add) { ## the object "objName" should be increased by "add", ## but the evaluation should be done in the calling function (here: fun1) ## ...... what's the right code?? } fun1 <- function() { x <- 1 fun2("x",

Dear colleagues, I'm an epidemiologist with no background in programming and just started using R a few weeks ago. I am working on the epidemiology of African sleeping sickness, and am trying to use R to perform a Monte Carlo Markov Chain analysis to estimate three unknown parameters within a model of African sleeping sickness case detection that is mainly informed by actual field programme

Dear all, model.frame behaves in a way I don't expect when both its formula and subset argument are passed through a function call. This works as expected: model.frame(~wool, warpbreaks, breaks < 15) #> wool #> 14 A #> 23 A #> 29 B #> 50 B fun1 <- function(y) model.frame(~wool, warpbreaks, y) fun1(with(warpbreaks, breaks < 15)) #> wool #> 14

Full_Name: Yi-Xiong Zhou Version: 1.5.1 OS: win2000pro Submission from: (NULL) (64.169.249.42) Update() can not find objects when it is used in a function, which is in turn being called by another function. Here is a R script to show the problem: ######## begin of the test script ########## fun1 <- function() { x <- matrix(rnorm(500), 20,25) y <- rnorm(20) oo <- lm(y~x)

Hello, I know of some various methods out there to utilize multiple processors but am not sure what the best solution would be. First some things to note: I'm running dependent simulations, so direct parallel coding is out (multicore, doSnow, etc). I'm on Windows, and don't know C. I don't plan on learning C or any of the *nix languages. My main concern deals with Multiple

Hello, Rlisters I have to compute p-values that are on the tail of the distribution, P-values < 10^-20. However, my current implementations enable one to estimate P-values up to 10^-12, or so. A typical example is found below, where t is my critical value. ########### example - code adapted from Rassoc ####################### rho01 = 0.5 rho105 = 0.5 rho005 = 0.5 t = 8 z = 2