similar to: Column(row)wise minimum and maximum

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:

Hi, I wonder whether similarly to the very quick rowSums and colSums functions in the base package, one could add quick functions that calculate the min or max over rows / cols in a matrix. While apply(x,1,min) works, I found out by profiling a program of mine that it is rather slow for matrices with a very large number of rows. A quick functionality seems to be already there in the

Dear list, As a minimal test of a more complex grid layout, I'm trying to find a clean and efficient way to arrange text grobs in a rectangular layout. The labels may be expressions, or text with a fontsize different of the default, which means that the cell sizes should probably be calculated using grobWidth() and grobHeight() as opposed to simpler stringWidth() and stringHeight().

Hi, I have a cloud of randomly distributed points in 2-dimensional space and want to set up a grid, with a given grid-cell size, that minimizes the distance between my points and the grid nodes. Does anyone know of an R function or toolbox that somehow addresses this problem? This is a problem of optimizing the location of the grid, not a problem of deciding what should be the grid-cell size,

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. 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

I'm having a small problem using RODBC. I'm trying to retrieve a string from a very long memo field (512*20*9=9360 characters = 74880 bytes) in an MSAccess database. It appears that RODBC set a maximum buffer size for a single column of 65535 bytes. ########## cut from RODBC.c ########## } else { /* transfer as character */ int datalen = thisHandle->ColData[i].ColSize; if

hi, i am getting an error when calling the impute.knn function (see the screenshot below). what is the problem here and how can it be solved? screenshot: ################## *** caught segfault *** address 0x513c7b84, cause 'memory not mapped' Traceback: 1: .Fortran("knnimp", x, ximp = x, p, n, imiss = imiss, irmiss, as.integer(k), double(p), double(n), integer(p),

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,

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 <-

>>>>> jing hua zhao >>>>> on Mon, 24 Jun 2019 08:51:43 +0000 writes: > Hi All, > Thanks for all your comments which allows me to appreciate more of these in Python and R. > I just came across the matrixStats package, > ## EXAMPLE #1 > lx <- c(1000.01, 1000.02) > y0 <- log(sum(exp(lx))) > print(y0) ## Inf

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;

Hi, I have a list made up of character strings with each item a different length (each item is between 1and 6 strings long). Is there a way to convert a "ragged" list to a matrix such that each item is its own row? Here is a simple example: a=list(); a[[1]] = c("a", "b", "c"); a[[2]] = c("d", "e"); a[[3]] = c("f",

Hi, I would like to know how to capture the console output from running an algorithm for further analysis. I can capture this using capture.output() but that yields a character vector. I would like to extract the actual numeric values. Here is an example of what I am trying to do. fr <- function(x) { ## Rosenbrock Banana function on.exit(print(f)) x1 <- x[1] x2 <- x[2]

Hi, I have a right-censored (positive) random variable (e.g. failure times subject to right censoring) that is observed for N subjects: Y_i, I = 1, 2, ..., N. Note that Y_i = min(T_i, C_i), where T_i is the true failure time and C_i is the censored time. Let us assume that C_i is independent of T_i. Now, I would like to generate another random variable U_i, I = 1, 2, ..., N, which is

Hi, I would like to know whether there exist algorithms to compute the coefficients or, at least, the degree of the minimal polynomial of a square matrix A (over the field of complex numbers)? I don't know whether this would require symbolic computation. If not, has any of the algorithms been implemented in R? Thanks very much, Ravi. P.S. Just for the sake of completeness, a

Hi, I apologize for the previous posting, where the message was not formatted properly. Here is a better version: I have written the following function to check whether a vector has elements satisfying monotonicity. is.monotone <- function(vec, increase=T){ ans <- TRUE vec.nomis <- vec[!is.na(vec)] if (increase & any(diff(vec.nomis,1) < 0, na.rm=T)) ans <- FALSE

Dear R group, I have a data frame which contains data on preferences on 7 items (ranks 1 through 7) listed by each participant. I would like to tabulate this in a 7x7 table where the rows would be the items and the columns would be the number of times that item received a particular rank. I tried doing this by creating a matrix by "rbind"ing each vector obtained using

Hi, Please see the attached contour plot (I am sorry about the big file). This was created using the vis.gam() function in "mgcv" package. However, my question is somewhat broader. In generating this figure, I first created the contours using vis.gam() and then I plotted the points. These point are plotted on top of the contours so that some of the contour lines are only partially

Hi, I am running a simulation study with the survival::coxph. Some of the simulations result in problematic fits due to flat partial likelihood. So, you get the warning message: Warning message: In fitter(X, Y, strats, offset, init, control, weights = weights, ... : Loglik converged before variable 2 ; beta may be infinite. How can I keep track of the simulations which yield any kind of