similar to: How R converts data between objects

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

Hello, I have a C++ code for 2D random walks with traps and I want to convert it in a R code with its syntaxs, can anyone help??????? It's easy for me to adapt the body but I want help with the beginig (variable declaration) and th end exporting the output to a file ( like write.table() or sink() ) Thank you... #include <iostream> #include <math.h>#include

Hello everyone, I am using ggplot to plot but I am getting the following error which I do not understand Error: geom_text requires the following missing aesthetics: label My code is dimx<-256 library(ggplot2) dev.new() xandy<-expand.grid(seq(1:dimx),seq(1:dimy)) xx<-data.frame(xandy[[1]],xandy[[2]],Powermap=Powermap) subsetxx<-subset(xx, xx$Powermap>threshold)

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

Dear all, I am trying to learn lapply. I would like, as a test case, to try the lapply alternative for the Shadowlist<-array(data=NA,dim=c(dimx,dimy,dimmaps)) for (i in c(1:dimx)){ Shadowlist[,,i]<-i } ---so I wrote the following--- returni <-function(i,ShadowMatrix) {ShadowMatrix<-i} lapply(seq(1:dimx),Shadowlist[,,seq(1:dimx)],returni) So far I do not get same results

Dear all, I am attempting to convert 10 NetCDF files into a single NetCDF file, due to the data input requirements of a model I hope to use. I am using the ncdf package, version 1.6. The data are global-scale water values, on a monthly basis for 10 years (ie. 120 months of data in total; at present the data are separated by year, with 12 months of data in each file - mrunoff_1986 through to

Dear all, I am attempting to convert 10 NetCDF files into a single NetCDF file, due to the data input requirements of a model I hope to use. I am using the ncdf package, version 1.6. The data are global-scale water values, on a monthly basis for 10 years (ie. 120 months of data in total; at present the data are separated by year, with 12 months of data in each file - mrunoff_1986 through to

Hi, Please see the email exchanges below. I am having trouble generating output that is large enough for our needs, specifically when using the GaussRF function. However, when I wrote Dr. Schlather (the author of the GaussRF function), he indicated that there is also a limit imposed by R itself. Is this something that we can overcome? Thank you very much for any assistance you may provde.

Dear all I would like to ask you if an assignment can be done inside a lapply statement. For example I would like to covert a double nested for loop for (i in c(1:dimx)){ for (j in c(1:dimy)){ Powermap[i,j] <- Pr(c(i,j),c(PRX,PRY),f) } } to something like that: ij<-expand.grid(i=seq(1:dimx),j=(1:dimy)) unlist(lapply(1:nrow(ij),function(rowId) { return

Hello everyone. I am trying to build up understanding in R by trying to develop just some simple scenarios. I would like to explain you what I am trying to do and what I did so far. I would like to put inside a RasterLayer (raster package) a Gaussian field (for given covariance) using grf function (geoR package) 1. First I created a Raster Layer object r <- raster() # Default values are

Dear All, Can anyone please let me know how exactly ncdf deals with NaN. I am trying to pass in a vector of data that has some NaN in it, into a variable in NetCDF. dimX <- dim.def.ncdf("X","count",(1:6)) dimY <- dim.def.ncdf("Y","count",(1:3)) var1 <-

R developers, I have a first attempt to make an aov function. Eventually I want to build in Error() structure, but first I am trying to get this presentable for balanced data with only a single stratum, just using residual error. I am following R. M. Heiberger's Computation for the Analysis of Designed Experiments, Wiley (1989) I a using a wrapper (aov.bal) to call the

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

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

I am trying to rasterize a SpatialPolygon object by a RasterBrick object. The documentation of the raster::rasterize function explicitly says this is allowed. Here's what I am doing # load the raster package library("raster") # create a raster brick object using the example from the brick function documentation b <- brick(system.file("external/rlogo.grd",

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, 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, 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

I have a raster (which I called glc), which I read into R as a raster with "raster.create.from.file" from the raster package (R-forge). Values in glc range between 1 and 27 (whole numbers only). I'd like to extract all cells with a value of 1 to create a new raster with only the cells that have a value of 1, or to extract the xy values of all raster cells with a value of 1. Could you

Dear R devel, Apologies for these (most probably trivial) questions, doing my first attempt to call C from R (and actually learning C in the process). I need to pass a matrix to C, and after reading R-exts.pdf (many times), I was unable to find how to handle matrices at C-level... except for, what probably is the answer, that matrices are in fact vectors with dimensions. This is a sample code I