similar to: How to generate a random field with truncated marginal distributions?

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

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

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.

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

hello all, Quick question, how do I get the p value out of the anova? Thanks, Paul > pb<-aov(as.numeric(diff[5,16:33]) ~ grF) > summary(pb) Df Sum Sq Mean Sq F value Pr(>F) grF 3 2.7860e+10 9.2867e+09 4.2236 0.02534 * Residuals 14 3.0783e+10 2.1988e+09 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 '

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

Dear friends. I have trobpe with path. Below I have tried to paste a path and file name but it will not work > path <- "D:/rw0641/own/own/procedures/GFR_TIME" > path [1] "D:/rw0641/own/own/procedures/GFR_TIME" > path1 <- paste(path,"gfr.txt",sep="/") > path1 [1] "D:/rw0641/own/own/procedures/GFR_TIME/gfr.txt" -- this is good

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 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 would like to generate pseudo-random numbers between two numbers using R, up to a given distribution, for instance, rnorm. That is something like rnorm(HowMany,Min,Max,mean,sd) over rnorm(HowMany,mean,sd). I am wondering if dnorm(runif(HowMany, Min, Max), mean, sd) is good. Any idea? Thanks. -james

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

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 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 *

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

Hi, I want to automatically fit variograms to a large number of different sample data sets, and call the funtion "likfit" (in package geoR) from within a for-loop. "likfit" does again call "optim". After ssuccessfully fitting variograms to some of the data sets, the procedure crashes and I get the error message: Error in optim(par = ini, fn = negloglik.GRF,

Hi, This is my code (my data is attached): library(languageR) library(rms) library(party) OLDDATA <- read.csv("/Users/Abigail/Documents/OldData250412.csv") OLDDATA$YD <- factor(OLDDATA$YD, label=c("Yes", "No"))? OLDDATA$ND <- factor(OLDDATA$ND, label=c("Yes", "No"))? attach(OLDDATA) defaults <- cbind(YD, ND) set.seed(47) data.controls

Hi, I have a dataframe with some 800 rows and 14 columns. Could you please advise how I can concatenate the rows - one after another. Similarly for columns, one below the other. Many thanks. Cheers, Santana [[alternative HTML version deleted]]

Hi All, I am new in R and trying to simulate random normal 2D field with mean trend say north-south. My domain is 10x10 grid and I am trying to use mvnorm but do not know how to specify the domain and the mean field. I would appreciate any help. Cheers, SK --------------------------------- [[alternative HTML version deleted]]