search for: rowmin

...here in the functions pmax and pmin, but it is rather cumbersume to apply them to all columns of a matrix (if one does not know how many columns the matrix has). Below, I have some code that shows a very unelegant implementation that illustrates possible speed gains if apply could be avoided: rowMin = function(x) { # Construct a call pmin(x[,1],x[,2],...x[,NCOL(x)]) code = paste("x[,",1:(NCOL(x)),"]",sep="",collapse=",") code = paste("pmin(",code,")") return(eval(parse(text=code))) } # Speed comparison: Taking rowM...

Hi, Currently, the "base" has colSums, colMeans. It seems that it would be useful to extend this to also include colMin, colMax (of course, rowMin and rowMax, as well) in order to facilitate faster computations for large vectors (compared to using apply). Has this been considered before? Please forgive me if this has already been discussed before. Thanks, Ravi Ravi Varadhan, Ph.D. Assistant Professor The Center on Aging and Health Divisio...

Hi. On Sun, Feb 13, 2011 at 10:18 AM, TakeoKatsuki <takeo.katsuki at gmail.com> wrote: > > Hi Henrik, > > It would be nice if functions of the matrixStats package can handle array > data. > For example, rowSums() of the base package sums along the third axis of an > array by rowSums(x, dim=2). That is a good idea. This was indeed the initial objective before starting

Hello all, I have a question which I have been struggling with for several weeks now, that I think might be easy for more proficient coders than myself. I have a large behavioral dataset, with behaviors and the times (milliseconds) that they occurred. Each subject has a separate file, and a sample subject file can be generated using the following syntax: Time <- c(1000, 1050, 1100, 1500, 2500,

I can repeatably crash R (segfault) by doing n <- 10 z <- data.frame(a=1:n,b=1:n) View(z) and then hitting the "End" key on my keyboard. I haven't got debugging going yet, but running under gdb (without debugging symbols) does give this: 0xb7b63583 in strlen () from /lib/tls/i686/cmov/libc.so.6 R version 2.6.2 (2008-02-08) i486-pc-linux-gnu [Ubuntu Gutsy] locale:

Dear expeRts, I have two matrices A and B. They have the same number of columns but possibly different number of rows. I would like to compare each row of A with each row of B and check whether all entries in a row of A are less than or equal to all entries in a row of B. Here is a minimal working example: A <- rbind(matrix(1:4, ncol=2, byrow=TRUE), c(6, 2)) # (3, 2) matrix B <-

.... Most of the major groundwork has already been done, but there is still room for improvements. If you're using matrixStats functions in your package already now, you should see some notable speedups for those function calls, especially compared to what was available back in June. For instance, rowMins() is now 5-20 times faster than functions such as base::pmin.int() whereas in the past they performed roughly the same. I've also added a large number of new package tests; the R and C source code coverage has recently gone up from 59% to 96% (? and counting) [https://coveralls.io/r/HenrikBen...

.... Most of the major groundwork has already been done, but there is still room for improvements. If you're using matrixStats functions in your package already now, you should see some notable speedups for those function calls, especially compared to what was available back in June. For instance, rowMins() is now 5-20 times faster than functions such as base::pmin.int() whereas in the past they performed roughly the same. I've also added a large number of new package tests; the R and C source code coverage has recently gone up from 59% to 96% (? and counting) [https://coveralls.io/r/HenrikBen...

Hi together, I have some data in a matrix structure - say 1000 rows with 10 columns. And I like to do some calculations (like max, avg or min) on row level. The only solution I found so fare was using a loop like for (i in 1:1000) { max[i] <- max(matrix[I,]) } It looks like that this is not very fast. Does an other way exists? Kind regards Ulrich

Hi Developers, I am looking for another new project to help me get more up to speed on R and to learn something outside of R internals. One recent R issue I have run into is finding a fast implementations of the equivalent to the following SAS code: /* MDPC is an integer sort key made from two integer columns */ MDPC = (MD * 100000) + PCO; /* sort the dataset by the key */ PROC SORT;

Thank you very much for your reply. Your code work well with this example. I modified a little to fit my real data, I got an error massage. Error in split.default(x = seq_len(nrow(x)), f = f, drop = drop, ...) : Group length is 0 but data length > 0 On Thu, Jan 31, 2013 at 12:21 PM, arun kirshna [via R] < ml-node+s789695n4657196h87@n4.nabble.com> wrote: > Hi, > Try this: >