similar to: unavoidable loop? a better way??

Hi All, I would like to compute the simple finite-difference approximation to the gradient of a scalar function of a large number of variables (on the order of 1000). Although a one-time computation using the following function grad() is fast and simple enough, the overhead for repeated evaluation of gradient in iterative schemes is quite significant. I was wondering whether there are

Hi All, I want to do something along the lines of: for (i in 1:n){ for (j in 1:n){ A[i,j]<-myfunc(x[i], x[j]) } } The question is what would be the most efficient way of doing this. Would using functions such as sapply be more efficient that using a for loop? Note that n can be a few thousand. Thus atleast a 1000x1000 matrix. Thanks, Sachin [[alternative HTML version

Hello, everyone I have a piece of code that looks like this: mrets <- merge(mrets, BMM.SR=apply(mrets, 1, MyFunc, ret="BMM.AV120", stdev="BMM.SD120")) mrets <- merge(mrets, GM1.SR=apply(mrets, 1, MyFunc, ret="GM1.AV120", stdev="GM1.SD120")) mrets <- merge(mrets, IYC.SR=apply(mrets, 1, MyFunc, ret="IYC.AV120",

Dear all, I'm having a problem with debugger() in both R 2.8.0 and R 2.12.0. Probably also versions in-between. I don't see it logged in the bug database, but it's hard for me to imagine that no-one else has encountered it. So my question is whether it's a known problem with a workaround, or do I log it as a new problem? The situation is that if I use

Can someone help me with this simple example? sq <- function() { y <- x^2 y } myfunc <- function() { x <- 10 sq() } myfunc() executing the above in R yields: > myfunc() Error in sq() : Object "x" not found I understand that R's scoping rules cause it to look for "x" in the environment in which "sq" was defined (the global environment in

David Brahm <brahm at alum.mit.edu> wrote: >VBMorozov at lbl.gov wrote: >> I would like to pass variables to a function in R in "by reference"... >Just in case the ensuing discussion got too esoteric, here's one simple answer: >R> x <- 1:10 >R> MyFunc <- function(x, zz) assign(deparse(substitute(zz)), sum(x), 1) >R> MyFunc(x,y) >R>

I am trying to rename column names in a dataframe within a function. I am seeing an error (listed below) that I don't understand. Would be grateful of an explanation of what I am doing wrong and how I should rewrite the function to allow me to be able to rename my variables. Thanks. # Test Function myfunc <-function(var){ d = c(1,2,3,4,5) dts =

I may be doing this wrong! but I have a function which I have simplified a lot below. I want to pass some 'field names' of a data-frame to the function for it to then do some manipulation of. Here's my code: #build a simple dataset mydataset = data.frame (

Dear R-guRus: I would like to pass variables to a function in R in "by reference", e.g Fortran style. For example, suppose I have the following code x<-c(1:10) y<-1 MyFunc<-function(x,y) {y<-sum(x); return(NULL)} MyFunc(x,y) print(y) in this case print(y) will produce "1" instead of 55 (which is sum(x)) - how do I make sure that afte the function is run, y

Dear R-guRus: I would like to pass variables to a function in R in "by reference", e.g Fortran style. For example, suppose I have the following code x<-c(1:10) y<-1 MyFunc<-function(x,y) {y<-sum(x); return(NULL)} MyFunc(x,y) print(y) in this case print(y) will produce "1" instead of 55 (which is sum(x)) - how do I make sure that afte the function is run, y

Dear all, It has been ages since I studied integration in college. Right now I try to recover all this kind of knowledge and then try to understand how integration works. Thus I am doing some first 'experiments' and I would like to request your help and comments. I have the function: p2<-function(x){0.5*(3*x^2-1)} # I found the square of p2 by using some pencil and

R-help, I am trying to create a function that i pass a data set to and have the function return some calculations based on data. Allow me to illustrate: myfunc <- function(lst,mn,sd){ lst <- sort(lst) mn <- mean(lst) sd <- sqrt(var(lst)) return(lst,mn,sd) } data1 <-c (1,2,3,4,5) data2 <- c(6,7,8,9,10) myfunc(data1,data1mn,data1sd) myfunc(data2,data2mn,data2sd)

Hi all, is there a way to extract the name of a function, i.e. do the reverse of match.fun applied to a character string? I would like to print out the name of a function supplied to another function as an argument. For example: myFunc = function(x) { x+1 } applyFunc = function(fnc, x) { fnc = match.fun(fnc) fnc(x) } Is there a way to obtain "myFunc" from the argument fnc in

Hi, I would like to evaluate a function, with 3 arguments, for instance, myfunc<-function(a,b,c) { sqrt(a)-exp(b)+4*c } How to execute myfunc(x,y,z), for all x, all y and all z, where x,y,z are vectors? Thank you very much in advance -- View this message in context: http://r.789695.n4.nabble.com/Execute-a-function-tp4637182.html Sent from

Hello, I have a function with a long list of parameters (of different types, numeric and string) myFunc <-function(p1, p2, p3, p4, p5...etc) { do.something(p1,p2,....) } I want to loop over this to provide a different set of parameters to the list every time. for (ii in 1:N) { myFunc(p1(ii), p2(ii),....etc) } I would like to simplify the notation and use some kind of structure, maybe

I've written a function, myFunc, that works fine with myFunc(data, ...), but when I use apply() to run it with an array of data apply(myArray, 1, myFunc, ...) I get a strange error: Error in match.fun(FUN) : '1' is not a function, character or symbol which really puzzles me because '1' is meant to be the margin of the array I want to apply over, but how come does apply()

When using ORC JIT, I'm having trouble with external function resolution (that is, of a function defined in the app, with C linkage). I add a declaration for the function to my IR, and when I use MCJIT, it finds it and all is well, But when I use ORC JIT (I *think* correctly, at least it closely matches what I see in the tutorial), I get an LLVM error, "Program used external function

Hello! I am trying to create an R optimization routine for a task that's currently being done using Excel (lots of tables, formulas, and Solver). However, otpim seems to be finding a local minimum. Example data, functions, and comparison with the solution found in Excel are below. I am not experienced in optimizations so thanks a lot for your advice! Dimitri ### 2 Inputs:

I have a question about what to do in the following situation (please bear with the setup): Package A defines an S4 generic 'foo' and as well as S4 methods for 'foo' and has exportMethods("foo") in its NAMESPACE file. Package B defines another method for 'foo' for class "bar" and has importFrom(A, "foo") exportMethods("foo")

Hello, I'm trying to combine the parallel computing power available through NVIDIA CUDA (www.nvidia.com/cuda) from within R. CUDA is an extension to the C language, so I thought it would be possible to do this. If I have a C file with an empty function which includes a needed CUDA library (cutil.h) and compile this to an .so file using a NVIDIA compiler (nvcc), called 'myFunc.so' I