similar to: Generating data from Null Distribution

Hi Given three vectors x <- c(fish=3, dogs=5, bats=2) y <- c(dogs=1, hogs=3) z <- c(bats=3, dogs=5) How do I create a multi-way table like the following? > out x y z bats 2 0 3 dogs 5 1 5 fish 3 0 0 hogs 0 3 0 ('out' is a matrix). See how the first line shows 'x' has 2 bats, 'y' has zero bats, and 'z' has 3 bats and so on for each

Hi I deal with long vectors almost all of whose elements are zero. Typically, the length will be ~5e7 with ~100 nonzero elements. I want to deal with these objects using a sort of sparse vector. The problem is that I want to be able to 'add' two such vectors. Toy problem follows. Suppose I have two such objects, 'a' and 'b': > a $index [1] 20 30 100000000

Hi I read that bundles are to be deprecated in 2.10. The BACCO bundle contains three packages (emulator, calibrator, approximator) which I am happy to unbundle. But the 'BACCO' moniker has some considerable cachet for me in terms of recognizability (eg with grant-giving bodies), as it has become an umbrella term for a whole bunch of related statistical functionality of which the three

Hi R-2.8.1, Suse 11.1 I'm having problems with pdf(). In the following transcript, file 'f.pdf' does not use the expected symbols for the plot. It uses a 'q' letter instead of the open circle I get when viewing the graphics window. I also get the same under r47678. Does anyone else get this? le112:~/scratch/R-2.8.1% R --vanilla --quiet >

Hi R-2.8.1, Suse 11.1 I'm having problems with pdf(). In the following transcript, file 'f.pdf' does not use the expected symbols for the plot. It uses a 'q' letter instead of the open circle I get when viewing the graphics window. I also get the same under r47678. Does anyone else get this? le112:~/scratch/R-2.8.1% R --vanilla --quiet >

Hello everyone. R-2.12.0, suse linux 11.3. I am debugging a package that uses S4 methods and R CMD check gives the following warning: > Warning in methods::findMethods(g, env) : > non-generic function 'mdm' given to findMethods() > See the information on DESCRIPTION files in the chapter 'Creating R > packages' of the 'Writing R Extensions' manual. I

Hi I have a data frame with timeseries information like this: year cell Q1 Q2 Q3 Q4 1940 1 1.2 1.4 1.4 1.9 1941 1 2.9 2.1 3.4 2.4 1942 1 2.7 3.2 1.5 2.6 1940 2 1.4 2.1 2.6 2.4 1941 2 2.4 1.4 1.4 3.4 1942 2 1.4 2.4 2.5 4.4 where the Qs mean 'quarter'. I want to extract from

Hi suppose I have a factor 'x': > x <- as.factor(c(rep("a",3),"b","d")) > table(x) x a b d 3 1 1 > > But this is not what I want because I need to include the fact that the count of "c" is zero. I can't just change the levels of x: > levels(x) <- c("a","b","c","d") >

Hi I want a generalization of tabulate() which works on rows of a matrix. Suppose I have an integer matrix 'observation': > observation y1 y2 y3 1 4 0 1 4 0 2 0 3 4 1 0 0 5 0 0 1 4 2 0 3 Each row corresponds to a (multivariate) observation. Note that the first two rows are identical: this means that data "c(1,4,0)" was observed twice. Now suppose I can list the sample

Hi, Can anyone guide me as to how I can add points to a p3d() plot from the onion package?? I want to plot points with different colors on the same 3D plot.? Perhaps I can do this without adding points but somehow directing the 'h' parameter to give different color to points based on a factor I assign to them? FYI, I can do this using using scatterplot3d() and points3d(), but these plots

Dear R-user I need a function to approximate a complex integration. My function is: aprox2=function(s,x,rate){ dexp(x,rate)*exp(-s*x) } where argument s is a complex number. I can't use the integrate function because it's only used with "numeric" arguments Does anyone know some function to approximate complex integrals? Thanks Borja [[alternative HTML version deleted]]

Folks, I put up a brief note describing my naive attempts to compute Goldbach partitions, starting with a brute-force approach and refining progressively. http://jostamon.blogspot.com/2009/02/goldbachs-comet.html I'd welcome your suggestions on improvements, alternatives, other optimisations, esp. to do with space vs time tradeoffs. Is this an example interesting enough for

I'm looking for some tutorial on S4. I only find the following one, which is not in English. Can somebody let me know if there is any introductory material? I'm very familiar with OO and C++. If there is some material that suits my background, it will be great. https://stat.ethz.ch/pipermail/r-help/2009-January/184108.html

Hi there I know, I'm sure you discussed this stuff 100 times, but I really have a basic understanding problem, if and how do I create a multidimensional array in R. I'm coming from MATLAB and there it's as easy as you ever could imagine. Ok, so, I want to have an array, where I can fill in data from a Excel spreadsheet. The array should be addressed like this:

Dear R family I am attempting to create a matrix. e.g., 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 How could I write a R program? Later I want to extend it to a N by N case. Thanks in advance best Moohwan

Hi [R-2.10.0; suse linux] I am having difficulty creating S4 objects in the data directory of a package. I want to create a bunch of simple S4 objects for use in the examples section of the Rd files. It says in R-exts that: " R code should be ?self-sufficient? and not make use of extra functionality provided by the package, so that the data file can also be used without having to load

Hi all Here is a rather naive implementation of the SEPA algorithm for generating lexical permutations: lexperm3 <- function(x, n=length(x)) { perms <- list() k <- 1 perms[[k]] <- x k <- k + 1 for (y in 1:(factorial(n)-1)) { i <- n-1 while (x[i] > x[i+1] && i > 0) { i <- i - 1 } # i is largest index st x[i] > x[i+1] j <- n #

Hi all, I try to generate sets of strategies that contain probability distributions for a defined number of elements, e.g. imagine an animal that can produce 5 different types of offspring and I want to figure out which percentage of each type it should produce in order to maximize its fitness. In order to do so, I need to calculate the fitness for all potential strategies. As an example, if I

Dear R, I have a covariates matrix with 10 observations, e.g. > X <- matrix(rnorm(50), 10, 5) > X [,1] [,2] [,3] [,4] [,5] [1,] 0.24857135 0.30880745 -1.44118657 1.10229027 1.0526010 [2,] 1.24316806 0.36275370 -0.40096866 -0.24387888 -1.5324384 [3,] -0.33504014 0.42996246 0.03902479 -0.84778875 -2.4754644 [4,] 0.06710229 1.01950917

In an effort to select the most appropriate number of clusters in a mixture analysis I am comparing the expected and actual membership of individuals in various clusters using the Fisher?s exact test. I aim for the model with the lowest possible p-value, but I frequently get p-values below 2.2e-16 and therefore does not get exact p-values with standard Fisher?s exact tests in R. Does anybody know