similar to: Why doesn't outer work?

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 =

Dear all, bigKRLS, which has been on CRAN for a couple of years, had to be pulled recently due to what seems to be a sanitizer issue stemming from its use of bigmemory. bigKRLS works fine (we?ve used it ourselves on many different platforms and have had over 15,000 downloads without an end user reporting difficulties because of this issue). Unfortunately, we have been unable to reproduce the

Hi, I'm trying to plot something in the following way and would like if you could help: I'd like in a same plot window, two plots are shown, the left one is a bird-view plot of the whole data, the right half keep changing, i.e., different plots will be shown up on request, so that when I select/click on some where in the left plot, the right plot will be the corresponding plot. What I

# Your mailer is set to "none" (default on Windows), # hence we cannot send the bug report directly from R. # Please copy the bug report (after finishing it) to # your favorite email program and send it to # # r-bugs@r-project.org Reproducible Rterm crash. I drew a complicated graph, then resized the graphics window. R reported > Insufficient memory for resize. Killing device

All, We are researching approaches to parallel R with the end goal of running R in a distributed manner on a Linux cluster. We expect of course to do some work decomposing our problems to be task-parallel or data-parallel, but wouldn't mind getting an initial boost working with "embarrassingly parallel" code sections and one of the approaches below. Incidentally our environment

Dear R-help list members, I am hoping to get you help in reproducing a problem I am having That is only reproducible on a large-memory machine. Whenever I run the following lines, get a segfault listed below: *** caught segfault *** address 0x7f092cc46e40, cause 'invalid permissions' Traceback: 1: La.svd(x, nu, nv) 2: svd(X) 3: ginv(bigmatrix) Here is the code that I run:

Hi all, I have a vector that contains the row numbers of data taken from several filtering operations performed on a large data frame (20,000rows x 500cols). In order to output this subset of data, I've been looping through the vector containing the row numbers (keepRows). output <- data.frame(row.names = rownames(bigMatrix)) for(i in keepRows) { output <- rbind(output,

I have code and documentation that don't match, but R CMD check didn't flag it. in mspath.R mspath <- function(formula, # formula with observed Markov states ~ observation times (required) qmatrix, # matrix of 1s and 0s with indices of allowed transitions (diagonal is ignored) (required) misc = FALSE, ematrix = NULL, # matrix

Hi, I have read several threads about memory issues in R and I can't seem to find a solution to my problem. I am running a sort of LASSO regression on several subsets of a big dataset. For some subsets it works well, and for some bigger subsets it does not work, with errors of type "cannot allocate vector of size 1.6Gb". The error occurs at this line of the code: example <-

Hi, I use nnet for my classification problem and have a problem concerning the calculation of the final value for my validation data.(nnet only calculates the final value for the training data). I made my own final value formula (for the training data I get the same value as nnet): # prob-matrix pmatrix <- cat*fittedValues tmp <- rowSums(pmatrix) # -log likelihood

Hi, the function outer can not apply a constant function as in the last line of the following example: > xg <- 1:4 > yg <- 1:4 > fxyg <- outer(xg, yg, function(x,y) x*y) > fconstg <- outer(xg, yg, function(x,y) 1.0) Error in outer(xg, yg, function(x, y) 1) : dims [product 16] do not match the length of object [1] Of course there are simpler ways to construct a constant

Dear All, I?m using R package ?msm? to fit a multi state model to infection history data (counts of infections per month upto diagnosis of a particular disease (sink state is state 11). The observed transitions are as follows: to from 1 2 3 4 5 6 7 8 10 11 1 35192 3806 899 233 46 11 3 0 1 534 2 3801 790 249 69 15

Hi all, I try to run such a line in TableGen but seems it's not working. What is the problem with this code? multiclass cond { let Inst{1-0}=0b11 in defm fxy: ConditionalALU; .... } I receive this error: test.td:162:11: error: expected '=' in let expression let Inst{1-0}=0b11 in defm fxy: ConditionalALU; ^ -------------- next part -------------- An HTML

>>>>> Gebhardt, Albrecht <Albrecht.Gebhardt at aau.at> >>>>> on Sun, 19 Mar 2017 09:14:56 +0000 writes: > Hi, > the function outer can not apply a constant function as in the last line of the following example: >> xg <- 1:4 >> yg <- 1:4 >> fxyg <- outer(xg, yg, function(x,y) x*y) >> fconstg

Dear R Programmers, I am trying to run a Poisson regression on all pairs of variables in a data set and obtain the permutation distribution. The number of pairs is around 100000. It seems my code will take weeks to run, unless I try something else. Could you give me any suggestions on how to improve the speed of the code below, or any general suggestions on how I may accomplish this task. Thanks

Hello, good mornig, i have one questions, anybody know how to calculate svd of a matrix of the library bigmatrix with library irlba? thanks [[alternative HTML version deleted]]

I have a large file backed big. matrix, with millions of rows and 20 columns. The columns contain data that I simply need to tabulate. There are a few dozen unique values. and I just want a frequency count Test code with a small "big" matrix. library(bigmemory) library(bigtabulate) test <- big.matrix(nrow = 100, ncol = 10) test[,1:3]<- sample(150) test[,4:6]<-

> Or is this a bad idea? I don't like the proposal. I have seen code like the following (in fact, I have written such code, where I had forgotten a function was not vectorized) where the error would have been discovered much later if outer() didn't catch it. > outer(1:3, 11:13, sum) Error in outer(1:3, 11:13, sum) : dims [product 9] do not match the length of object [1]

Dear R Users, I have the following glm, which I am running several times in a loop (I am not including the full code): reduced_model <- NULL; full_model <- NULL; reduced_model <- try(glm.fit(X4,n,family=poisson(link="log"))) full_model <- try(glm.fit(X5,n,family=poisson(link="log"))); On some occasions, an error is produced, which is why I have attempted to work

Dear all, Currently I am trying to integrate a function which depends on four variables, two of which are given, one is given in the integrate function, so there is one variable to integrate on. The code is as follows: Pmatrix = function(th) { P = matrix(nrow=6, ncol=6, data=0) P[1,1] = P[2,1]=P[3,2]=P[4,3]=P[5,4]=P[6,5]= exp(-th) P[,6] = 1-exp(-th) return(P)} lim.verd =