similar to: size limit in R?

I don't understand what is happening! I have a (large) object sim1, an matrix list with dim c(101,101) where each element is an 3*3 matrix. I am subsetting that with a matrix coo, of dim c(100,2), of unique indices, but the resulting object has length 99, not 100 as expected. Code reproducing the problem follows: library(RandomFields) set.seed(123) sim0 <- GaussRF(x=seq(0, 100, by=1),

Dear all, In my 'simple' computer I was running some experiments to help me understand how faster a multicore lapply will be. I thought it might be interesting for some people to look at the results. Even though are not accurate, still might be a good indicator how much improvement there can be. A.Case. The classic: for 1:100 for (i in c(1:dimz)){ print(sprintf('Creating the %d

Hi, I get an "Conditional jump or move depends on uninitialised value(s)" from valgrind when using 'solve' in combination with some simple C-code. (I did not use other functions of R besides 'solve'.) In detail: running R --vanilla -d "valgrind --tool=memcheck --memcheck:leak-check=yes --num-callers=20 " and calling .Call("XXX") nd <- 3

I have asked the same question on stackoverflow but did not get a satisfying answer. I am trying to simulate a lognormal spatial random field but I need the simulated value in a certain range. So I need some easy to use functions to generate a truncated Gaussian field to start with. To be specific, I need a function like GaussRF from the RandomFields package or grf from the geoR package to

Hello, I am presently trying to get a feel for the various packages out there that allow me to both analyze and simulate random fields. The package RandomFields is nice, but there are still a few aspects of its implementation that are confusing to me and I was hoping someone could help clarify things for me. It could also be that my questions reflect a lack of knowledge pertaining to random

Dear all, I would like to ask your help understand the subsequent steps for making my program faster. The following code: Gauslist<-array(data=NA,dim=c(dimx,dimy,dimz)) for (i in c(1:dimz)){ print(sprintf('Creating the %d map',i)); Gauslist[,,i]<-f <- GaussRF(x=x, y=y, model=model, grid=TRUE,param=c(mean,variance,nugget,scale,Whit.alpha)) } creates 100 GaussMaps (each

Hi, I'm using RF to simulate a correlated variable with GaussRF set.seed=1 GaussRF(sim.kfinegrid, grid=F, model="exponential", param=c(0,0.5,0,0.2)) However when I simulate again using the same random seed I get different results. > set.seed=1 > summary(GaussRF(sim.kfinegrid, grid=F, model="exponential", param=c(0,0.5,0,0.2))) Min. 1st Qu. Median Mean 3rd

Not really a bug (I eventually discovered), but ... When running GaussRF()/DoSimulateRF() with n>1, the default pch="!" prints backspaces to the terminal, which causes problems (LaTeX barfs) if you're running this within an Sweave() job. Setting RFparameters(pch="") fixes the problem. Could put this in the documentation, but I don't know where you could

Dear all. I am using the mclapply function to split my code to the many cores my system has. It seems that is working fine. This is the parallel version of lcapply. The only problem that I seem to have is that the printf cannot print messages. The ideal to me is to have fro my function an output of the form Shadowlist<-mclapply(1:dimz, function(i) { print(sprintf('Creating the

All, I am working with a large dataset that tracks who watches what media at what time. I am working on media consumption data from a survey, and one of the batteries asks if [r] has consumed a particular form of media at X time over the course of a week, so the data looks like this: 5:00-5:30 [0,1] 5:30-6:00 [0,1]. 23:30-0:00

Dear List I am using the RandomFields package, and I have a problem when 2d-kriging, with an anisotropy, some values from an irregular network. It works well when : - 2d-kriging, without any anisotropy, some data from an irregular network - 2d-kriging, with and without anisotropy, some data from a regular network - 3d-kriging, with and without anisotropy, some data from a regular network Here is

Hello everyone. I would kindly request your help concerning how R converts data between different structrures. In the following example please keep attention on the following two 1) I create f <- GaussRF(x=x, y=y, model=model, grid=TRUE,param=c(mean, variance, nugget, scale, alpha)) with image(x,y,f) and image(f) I get exactly the same image. then 2)I try to store f inside a raster

Hello, I am a graduate student of University of Florida. I am trying to run a process variation model and I am facing the following error. ------------------------------------------------------------- Analysis of geostatistical data For an Introduction to geoR go to http://www.leg.ufpr.br/geoR geoR version 1.6-27 (built on 2009-10-15) is now loaded

Hi R people: I am new to R. I am writing a function to (1) produce a sparse stochastic Gaussian 2D field and (2) perform a systematic transect sampling on this field, this carried out many times in a simulation framework. My function does a good job at producing the random field (a matrix of zeros and some manifestations of the stochastic process, depending on a parameter of the function

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Hello, Please excuse me if this question has been asked before. I'm new to R, and have been trying to google the answers without any success. I would like to convert a set of date and time into R date-time class. Right now, the dates and times are in integer format, so I first need to convert them into string, and then to R date-time using strptime. However, I have a problem converting them

Martin Maechler asked me to restate my problem. Compiling the file (slightly modified w.r.t. the formerly attached file) that can be found at http://www.geo.uni-bayreuth.de/~martin/filetransfer/xswms2d.Rd terminates with the error message ******* Syntax error: mismatched or missing brackets in /----- ..... \----- when compiled by R CMD Rdconv -t txt xswms2d.Rd However, the file does

Hello all, I am trying to create a grid of large number of points 4096*4096, on processing the data I am writing it into a file. phi <- 0.5 N <- 4096 mu <- 90 sim<-grf(N*N,grid="reg",cov.model="spherical",cov.pars=c(1,phi),method="RF") sim$data <- (sim$data - mean(sim$data)) / sd(sim$data) local <- array(sim$data, dim=c(N,N)) sysvar <- local *

Hi, I have problems when calling fft_factor and fft_work directly in my C-code. Martin Maechler already told me that I should not do this -- anyway, the code works fine if the length of the vector is a multiple of 2,3,5. If the length of the vector has different form I get, in some cases, a segmentation fault when work and iwork are freed (I use C's malloc to allocate memory for work and

Dear R Core Team, I'm wondering whether it is useful that every package should allow for help(PackageName), giving some general information about the package (more/nicer than library(help=PackageName)). This man page may include some mathematical or statistical background, or contain a guideline for the package. Cheers, Martin -- Martin Schlather email: