similar to: MARGIN in base::unique.matrix() and base::unique.array()

Dear R developers, While improving duplicated.array() and friends and developing equivalents for the new ff package for large datasets I came across two questions: 1) is it safe to use duplicated.default(), unique.default() and match() on arbitrary lists? If so, we can speed up duplicated.array and friends considerably by using list() instead of paste(collapse="\r") 2) while

Hi, In man page for duplicated: Value: ?duplicated()?: For a vector input, a logical vector of the same length as ?x?. For a data frame, a logical vector with one element for each row. For a matrix or array, a logical array with the same dimensions and dimnames. When 'x' is a matrix or array, the returned value is NOT a logical array: > m <-

Hi, is there a function in R already doing what I try to do below: # Let 'x' be an array with *any* number of dimensions (>=1). x <- array(1:24, dim=c(2,2,3,2)) ... x <- array(1:24, dim=c(4,3,2)) i <- 2:3 ndim <- length(dim(x)) if (ndim == 1) y <- x[i] else if (ndim == 2) y <- x[i,] else if (ndim == 3) y <- x[i,,] else ... and so on. My current

I was wondering if it is possible to do the following in a smarter way. I want get the mean value across the columns of a matrix, but I want to do this on subrows of the matrix, given by some vector(same length as the the number of rows). Something like nObs<- 6 nDim <- 4 m <- matrix(rnorm(nObs*nDim),ncol=nDim) fac<-sample(1:(nObs/2),nObs,rep=T) ##loop trough different

I have 3 nested functions, the core function goes like this: listx<-function(x,tox) { xt=table(x) wa=sort(unique(x,fromLast=FALSE)) print(xt) print(wa) ...... return(kk) } listx get called in functionB, and functionB get called in functionC. When I test functionB, the listx function works just fine. When I call functionB from functionC, strange thing happened: My result for xt: 0.07 0.17

Hi, I am trying to read in a rather large list of transactions using the arules library. It seems in the coerce method into the dgCmatrix, it somewhere calls unique. Unique.c throws an error when n > 536870912; however, when 4*n was modified to 2*n in 2004, the overflow protection should have changed from 2^29 to 2^30, right? If so, how would I change it in my copy? Do I have to recompile

I want to call dll from R but encounter problem in compiling the fortran program. First I try "Rcmd shlib prog.f", it failed and warning: make[1]: `libR.a' is up to date. make: *** No rule to make target `'prog.o', needed by `prog.a'. stop. Then I try to compile it by absoft fortran compiler, it works and produces prog.dll. But when this routine is called in R, it

Dear R-devel, May I suggest that a new feature be added to a couple of unique() methods? Sometimes it's useful to have the indices of the original data that the unique elements come from, so that the original data can be recreated from the unique()ed data. I suggest that an `index' argument be added for unique. Below is a suggested patch against R/src/library/base/R/duplicated.R: ***

Is it just my installation or bug in 1.9.0 ? The same thing works fine in 1.8.1 Best regards, Ryszard # R-1.9.0 library(pls.pcr) nr <- 8; ndim <- 2 x <- matrix(rnorm(nr*ndim), nrow=nr) y <- as.matrix(x[,1]) for (i in 2:ndim) y <- y + x[,i] y <- y + rnorm(length(y)) m <- pls(x,y,validation='CV') # Error in paste(ncomp, "LV's") : Argument

Dear all The trouble with the current duplicated() function in is that it can report duplicates while searching fromFirst _or_ fromLast, but not both ways. Often users will want to identify and extract all the copies of the item that has duplicates, not only the duplicates themselves. To take the example from the man page: > data(iris) > iris[duplicated(iris), ] ##duplicates while

--- src/library/base/man/unique.Rd | 2 +- 1 files changed, 1 insertions(+), 1 deletions(-) diff --git a/src/library/base/man/unique.Rd b/src/library/base/man/unique.Rd index a8397c7..4664a34 100644 --- a/src/library/base/man/unique.Rd +++ b/src/library/base/man/unique.Rd @@ -29,7 +29,7 @@ unique(x, incomparables = FALSE, \dots) \item{x}{a vector or a data frame or an array or

Dear all, I was playing around with factor contrasts, and found the argument nmax on function factor. When using nmax=1, R froze completely, and I had to close it from task manager. After some debugging, I found that the problem is actually in unique-function, where the internal unique function is called: .Internal(unique(x, incomparables, fromLast, nmax)) More generally, it looks like

To Whomever is Interested, I have spent several days searching the web, help files, the R wiki and the archives of this mailing list for a solution to this problem, but nonetheless I apologize in advance if I have missed something obvious. The problem is this; I have a 5-column data frame with about 4.2 million rows, and want to create a new (and hopefully much smaller) data frame that

R-listers, In using the adapt function, I am getting the following warning: Ifail=2, lenwrk was too small. -- fix adapt() ! Check the returned relerr! in: adapt(ndim = 2, lower = lower.limit, upper = upper.limit, functn = pr.set, Would someone explain what the 'lenwrk' value indicates in order to help diagnose this issue. Also, what are the possible codes for Ifail, so I can set

Hi all I was wondering if someone could help me. I have to estimate some parameters, so I am using the function nlm. Inside this function I have to integrate, hence I am using the function adapt. I don't understand why it is giving the following warnings: At the beginning: Warning: a final empty element has been omitted the part of the args list of 'c' being evaluated was:

Hello, I am trying to call a FORTRAN subroutine from R. The Fortran code is @: http://lib.stat.cmu.edu/apstat/206 It performs a bivariate isotonic regression on a rectangular grid (m X n) matrix. I used the g77 compiler and successfully created a dll file and it also loads successfully from R. But somehow the programs fails to run properly. (I do get the correct result when I compile the

Dear All, There is one problem I encountered when I used ADAPT to compute some 2-D integral w.r.t beta density. For example, when I try to run the following comments: fun2<-function(theta){return(dbeta(theta[1],0.005,0.005)*dbeta(theta[2],0.005,0.005))} int.fun2<-adapt(ndim=2,lo = c(0,0), up = c(1,1),functn = fun2,eps = 1e-4) It seems it will take very long time to run. Acturally, I

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)

Hi everybody. I want to identify duplicate numbers and to increase a value of 0.01 for each time that it is duplicated. Example: x=c(1,2,3,5,6,2,8,9,2,2) I want to do this: 1 2 + 0.01 3 5 6 2 + 0.02 8 9 2 + 0.03 2 + 0.04 I am trying to get something like this: 1 2.01 3 5 6 2.02 8 9 2.03 2.04 Actually I just know the way to identify the duplicated numbers rbind(x, duplicated(x) |

Hi, FWIW: /* Taken from R/src/main/unique.c */ static int requal(SEXP x, int i, SEXP y, int j) { if (i < 0 || j < 0) return 0; if (!ISNAN(REAL(x)[i]) && !ISNAN(REAL(y)[j])) return (REAL(x)[i] == REAL(y)[j]); else if (R_IsNA(REAL(x)[i]) && R_IsNA(REAL(y)[j])) return 1; else if (R_IsNaN(REAL(x)[i]) && R_IsNaN(REAL(y)[j])) return 1;