similar to: mapply

Hello, I am importing some raw voltage multichannel measurements into an R data frame. I need to scale each column with the respective sensitivity for that channel. I figured how to do it, but I am curious if there isn't a more elegant way. Now I start with something like this: rawdata <- data.frame(rbind(c(1,2,3), c(4,5,6))) sens <- c(2,4,6) and I do this: data <-

Dear ExpRts, I would like to perform a function with two arguments over the rows of two matrices. There are a couple of *applys (including mApply in Hmisc) but I haven't found out how to do it straightforward. Applying to row indices works, but looks like a poor hack to me: sens <- function(test, gold) { if (any(gold==1)) { sum(test[which(gold==1)]/sum(which(gold==1))) } else NA }

Hi group! Suppose I have 2 matrices A and B of equal dimensions. I want to apply a function f to all corresponding pairs of rows from A and B in an efficient manner. Basically, I want mapply(f, data.frame(A), data.frame(B)) but for rows. How do I do it? Thanks, Andrey

Hi, Starting with ordinary vectors, so we know what to expect: > mapply(function(x, y) {x * y}, 101:106, rep(1:3, 2)) [1] 101 204 309 104 210 318 > mapply(function(x, y) {x * y}, 101:106, 1:3) [1] 101 204 309 104 210 318 Now with an S4 object: setClass("A", representation(aa="integer")) a <- new("A", aa=101:106) > length(a)

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

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

Dear all, I am having a problem with mapply. I guess the reason is that mapply is not "vectorized". But could you please take a look at my code below and help me to find a solution (either a better way to use mapply or a different function to call). Thanks a lot! ##beginning of my code myfun <- function(threshold, all.data) { ##Just a demostration of a function that takes a

Hi, I have a problem that I'm finding a bit tricky. I'm trying to use mapply and assign to generate curried functions. For example, if I have the function divide divide <- function(x, y) { x / y } And I want the end result to be functionally equivalent to: half <- function(x) divide(x, 2) third <- function(x) divide(x, 3) quarter <- function(x) divide(x, 4) But I want

--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 >

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,

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

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

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

Hi all- I am wondering about using the mapply function to multiple data frames. Specifically, I would like to do a t-test on a subset of multiple data frames. All data frames have the same structure. Here is my code so far: f<-function(x,y) { test<-t.test(x$col1[x$col3=="num",],v$col2[x$col3=="num",],paired=T,alternative="greater") out<-test$p.value

Hi, I have a function myFun which I want to call multiple times, using different argument lists. myFun("v1", "2009", 1) myFun("v2", "2008", 1) myFun("q", "2001") How can I easily do this in R? Should I use mapply? I unsuccessfully tried something like: x <- list(c("v1", "2009", 1), c("v2",

Hi all!   My question in about using mapply instead for loop. Below is a example with for loop: Is it posible to give same results with mapply function?   Thanks for help!   OV   x <- 1:10 y <- 1:10 xyz <- data.frame(expand.grid(x,y)[1], expand.grid(x,y)[2], z = rnorm(100)) names(xyz) <- c("x", "y", "z") head(xyz) size <- 2 output <- NULL   ### for

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

Dear All, I would like to use the simprof function (clustsig package) but the available distances do not include Jaccard distance, which is the most appropriate for pres/abs community data. Here is the core of the function: > simprof function (data, num.expected = 1000, num.simulated = 999, method.cluster = "average", method.distance = "euclidean", method.transform =

To use the gauss.quad function: gauss.quad(n,type) which returns two lists $nodes and $weights whose length will each equal n. I'd like to do this for n=1 to 40 (type will not change) and have a dataset with 40 rows and 81 columns with all the nodes and weights. The first record would have N1 and W1 only and N2--N40 and W2--W40 would be missing. The last record would be full. I've