similar to: Location of Internal Code

Dear all, I'm wondering if there exist vectorized forms of 'isTRUE()', 'identical()' and 'all.equal()'. My problem is that I wish to test if each element of a vector is equal to a particular value (or numerically close), whilst dealing carefully with NAs and so on. However, using sapply() with identical() is very slow because it makes so many separate function calls:

Hello, I've run into a problem which is both maddening and rather hard to replicate, therefore I wondered if someone might know of a plausible explanation for it. I couldn't find anything in the archives, though maybe I'm searching for the wrong thing. I'm calling some C code using .C, and get the vector I'm interested in back as the 7th location in the returned list.

Hi [R-2.6.0, macOSX 10.4.10]. The helppage says that rowSums() and colSums() are equivalent to 'apply' with 'FUN = sum'. But I came across this: > a <- matrix(1:30,5,6) > is.integer(apply(a,1,sum)) [1] TRUE > is.integer(rowSums(a)) [1] FALSE > so rowSums() returns a float. Why is this? -- Robin Hankin Uncertainty Analyst National Oceanography Centre,

Hello, I have been using the TM package to create a TermDocMatrix, which I have saved as a matrix so that I can view word frequencies. Below is a section of the code that I have used and an excerpt of the output: What I wanted to be able to do is to view the output alphabetically - rather than the results being sorted by frequency as below, that an alphabetical list would be generated. This

Dear Contributors, I am asking help on the way how to solve a problem related to loops for that I always get confused with. I would like to perform the following procedure in a compact way. Consider that p is a matrix composed of 100 rows and three columns. I need to calculate the sum over some rows of each column separately, as follows: fa1<-(colSums(p[1:25,])) fa2<-(colSums(p[26:50,]))

Hi, I'm using colSums and rowSums to sum the first dimensions of arrays. It works ok but the resulting object is different. See > a3d <- array(rnorm(120, mean=2), dim=c(20,6,1)) > dim(colSums(a3d)) [1] 6 1 > dim(rowSums(a3d)) NULL > class(colSums(a3d)) [1] "matrix" > class(rowSums(a3d)) [1] "numeric" I was expecting rowSums to preserve the array

Hi, I have a matrix, say m=matrix(c( 983,679,134, 383,416,84, 2892,2625,570 ),nrow=3 ) i can find its row/col sum by rowSums(m) colSums(m) How do I divide each row/column by its rowSum/colSums and still return in the matrix form? (i.e. the new rowSums/colSums =1) Thanks. Casper -- View this message in context:

Hello, I am using tapply to pull out data by the day of week and then perform functions (e.g. mean). I would like to have the number of values used for the calcuation for the functions, sorted by each day of week. A number of entries in any given column are NAs. I have tried the following code and simple variants with no luck. for (i in 1:length(a[1,])){ x<-tapply(a[,i],a[,1],mean,

Dear Colleagues I am a very new member here. If my question sounds silly to you, I apologize in advance. If I have a complicated function without an explicit expression. ( For example, the price of American put option p is a function of the current stock price S and expected future volatility sigma, but there is no clean elementary function that would map (S, sigma) to p, in fact, p has to be

Hi, all! My project today is to write a speedy colSums(), which is a function available in S-Plus to add the columns of a matrix. Here are 4 ways to do it, with the time it took (elapsed, best of 3 trials) in both R and S-Plus: m <- matrix(1, 400, 40000) x1 <- apply(m, 2, sum) ## R=16.55 S=52.39 x2 <- as.vector(rep(1,nrow(m)) %*% m) ## R= 2.39 S= 8.52 x3 <-

The standard chisq.test() and fisher.test() functions, when applied to two distributions (to determine whether the same underlying distribution applies to both) requires one to pre-bin the distributions. Is there a library function (either built-in or in a package) that acts more like the ks.test() function, in that one can simply pass the two distributions and have it do the necessary binning as

Hi, I'd like to simply add column-wise using Matrix objects (Csparse). It looks like one can apply mosty any base function to these objects (i.e., apply, colSums), but there is a nasty conversion to traditional matrix objects if one does that. Is there any workaround? I can see colSum listed in the help for Class 'CsparseMatrix' , but I wonder whether I'm using the default

Working with an NxMxO sized matrix, currently I can do this in my code: if (max(colSums(array)) >= number) But to get an equivalent result using rowSums, I have to do: for (i in 1:10) { if (max(rowSums(array[,,i])) >= number) } I'm running both in a much larger loop that loops millions of times, so speed and such is quite a big factor for me. Currently, the colSums line uses about

> On Oct 21, 2017, at 7:50 AM, Martin Maechler <maechler at stat.math.ethz.ch> wrote: > >>>>>> C W <tmrsg11 at gmail.com> >>>>>> on Fri, 20 Oct 2017 15:51:16 -0400 writes: > >> Thank you for your responses. I guess I don't feel >> alone. I don't find the documentation go into any detail. > >> I also find

Hi, it's my first time to write a loop with R for my homework. This loop is part of the function. I wanna assign values for hll according to panel [ii,1]=pp. I didn't get any error message in this part. but then when I further calculate another stuff with hll, the function can't return. I think it must be some problem in my loop. Probably something stupid or easy. But I tried to look

One problem I've been having is the special case in which only one row/column remains and the variable gets converted into a vector when entries are removed by logical masking. This is a problem because subsequent code may rely on matrix operations (apply, colsums, dim, etc) For example: > a <- matrix(c(1, 2, 3, 4), nrow = 2) > a [,1] [,2] [1,] 1 3 [2,] 2 4 >

Dear all, ## I have 4 tables like this: satu <- array(c(5,15,20,68,29,54,84,119), dim=c(2,4), dimnames=list(c("Negative", "Positive"), c("Black", "Brown", "Red", "Blond"))) dua <- array(c(50,105,30,8,29,25,84,9), dim=c(2,4), dimnames=list(c("Negative", "Positive"),

Dear list, I just made a very simple mistake, but it was hard to spot. And I think that I should warn other people, because it is probably so simple to make... === R code === # Let us create a matrix: (a <- cbind(c(0,1,1), rep(1,3))) # [,1] [,2] # [1,] 0 1 # [2,] 1 1 # [3,] 1 1 # That is a MISTAKE: a/colSums(a) # [,1] [,2] # [1,] 0.0000000 0.3333333

I have a data frame called test shown below that i would like to summarize in a particular way : I want to show the column sums (columns y ,f) grouped by country (column e1). However, I'm looking for the data to be split according to column e2. In other words, two tables of sum by country. One table for "con" and one table for "std" shown in column e2. Finally at the

Hi, Here i have a matrix like this, OLDMatrix <- X1 X2 X3 ----- ------ ------ 22 24 23 25 27 27 10 13 15 the thing is, im running two function(SUM,COUNT) to get output in another matrix called NEWMatrix NEWMatrix <- c("SUM",colSums(OLDMatrix )) NEWMatrix <- c("COUNT",colSums(!is.na(OLDMatrix