similar to: Memory problems using Krige (sgeostat)

Dear all, I'm new on R and this mailing list. We work on spatial rainfall estimation with R and Grass. We have a problem with the krige function from the sgeostat package. We would like to limit the estimated area with a polygon limit. I use a 50 points polygon to describe my work area. The krige function work quiet well without limit. But if I use this option I have the following error

Greetings all, Ran into a strange problem with the krige function from geoR. The problem that I am having is that while the krige function seems to work well, the resulting predicted values are all NAs. Given the size of the datasets I am working with can't attach it, but I can provide snippets of the datasets. > casedata station year month day obs mpe bias type

Full_Name: Albrecht Gebhardt Version: 0.90.0 OS: Linux + Tru64 Submission from: (NULL) (143.205.61.72) This is where example(krige) from library(sgeostat) stops with R 0.90.0: krige> image(grid$x, grid$y, grid$krige$zhat, add = T) Error in length(x) == nrow(z) : comparison (1) is possible only for vector types in previous versions of R the above image() call worked, because image() did the

HI, I wonder does anyone have experience with doing sequential gaussian simulation with krige() function in gstat? I find it VERY slow compared to use krige() to achieve kriging function itself.. I wonder why, is that because it has to model the variogram, and do the kriging separately for each point to be simulated? so it would be N times slower to achieve the simulation than the kriging if

Hi, I'm using the function krige.conv from package geoR but I'm getting a warning that I do not understand: Warning messages: 1: NaNs produced in: sqrt(variance) 2: NaNs produced in: sqrt(variance) I'm performimg an ordinary kriging with a log transform (lambda=0). Does anyone knows where this comes from ? Thanks and regards EJ

Hello, I'm interested in doing Bayesian kriging using R. I see that the package geoR has a function that will allow one to do this (krige.bayes). However, my data are not in the form of points, but rather they are blocks that represent spatial averages (i.e., the number of fishing hooks per month in a given lat x long square). I am therefore interested in treating the data as

Dear all, When a conventional kriging and then a contour plot is limited with a polygon (as possible with krige.conv and contour.kriging), the polygon border is displayed in black by default. > kc<-krige.conv(CZdata,loc=pred.grid,borders=czpoly,krige=krige.control(obj.m=ls)) > plot(CZcoord,xlab="x",ylab="y",type="n",asp=1) >

ear ALL, The last part of my thesis analysis is the cross validation. Right now I am having difficulty using the cross validation of gstat. Below are my commands with the tsport_ace as the variable: nfold <- 3 part <- sample(1:nfold, 69, replace = TRUE) sel <- (part != 1) m.model <- x2[sel, ] m.valid <- x2[-sel, ] t<- fit.variogram(v,vgm(0.0437, "Exp", 26, 0)) cv69

Dear all, I would like to estimate rainfall with the krige function. First, I produce a polygon of my region of interest (where poly_test.txt is a x,y suite of points defining a polygon, obtained with the grass v.out.ascii command) : user>border_limite<-read.table("/home/lionel/rwork/poly_test.txt",header=FALSE) user>polygone<-list(x=border_limite[,1],y=border_limite[,2])

A majority of my data makes a kriged map perfectly using an exponential model for the semivariogram to fit my data and then going through the commands variofit() to define the model and then krige.conv() to use the model to predict values in a grid. But?one set of my data appears to be linearly correlated for the first 5000 meters and not correlated beyond that. I have been having problems

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Hello, I used glsm.mcmc and likfit.glsm to create model. Now I want to predict at different locations, but I can't get glsm.krige to work. I keep getting the error that trend.d and trend.l must have similar specifications. I have tried numerous ways to include the covariates in the glsm.krige model, and I keep getting the same error message. The bolded part is the part that doesn't work.

Hi all, I tried to produce some kriged surfaces with geoR (latest version). The size of the grid should be around 900 x 650 cells (what I find is not a very big grid), and the number of points is around 2500. The command krige.conv stopped after arround 5 min saying it can not allocate a vector with around 1.5 billion units. Sounds reasonable. Is there a workaround? How would I partition the

Hi everybody, I have a longitude vector and a latitude one. Associated to these coordinates, i have a matrix with some data at some coordinates but not all. Lon <- seq(136.025,144.975,0.05) Lat <- rev(seq(-66.975,-65.525,0.05)) dim(z) <- c(Lon,Lat) And i have tried to apply to these data a kriging function. But first i need to reshape these 3 variables to have a dataframe

Hi ALL, Can you please help me on how to determine the prediction error for ordinary kriging?Below are all the commands i used to generate the OK plot: rsa2 <- readShapeSpatial("residentialsa", CRS("+proj=tmerc +lat_0=39.66666666666666 +lon_0=-8.131906111111112 +k=1 +x_0=0 +y_0=0 +ellps=intl +units=m +no_defs")) x2 <- readShapeSpatial("ptna2",

Hi all, I use kriging to interpolate the precipitation from stations, but the map of this results show lots of stripes. (please see the attachment)I think there's something wrong with the setting of the dimension of this matrix, however, I have no idea how to know or test to see if this setting is correct or not.I've tried to switch the latitude and longitude, but still got the same

Hi list, Well, this time I’ve a doubt with mapping generation. I was already able to read and plot shapefiles, plot point on this map. All this without any problems, but now I want to do something that I think, only Golden Software Surfer is capable of. I would like to plot a kriging result on the existing map (map script below). Well I looked for on the net, but I was not able to be sure of

Hi, all, Have anyone used kringing included in R? How is it? Does it handle anisotropy data well? How does it compare with Kriging in Arc/Info? or other geostatistics software customized to do kriging or other geostatistics functions? I tried Easykriging, a geostatistics tool developed for Matlab. It has very nice GUI, but it does not provide library which i can call in my programs. so it is

Hi R-people: Working in geoRglm, it shows me, according to AIC criterion, that the non-spacial model describes the process in a better way. It's the first time that I'm facing up to. These are my results: OP2003Seppos.AICnonsp-OP2003Seppos.AICsp #[1] -4 (OP2003Seppos.lf0.p<-exp(OP2003Seppos.lf0$beta)/(1+exp(OP2003Seppos.lf0$beta))) #P non spatial #[1] 0.9717596

I'm performing ordinary and simple kriging from a set of non-negative values: >krige.control.sk<-krige.control(type.krige="sk",obj.model=my.variogram.model,beta=my.variogram.model$beta) >sk<-krige.conv(cadiz.geo,data=amostragem.cadiz$dia1.std,locations=as.matrix(cadiz.polygrid[,c(1,2)]),krige=krige.control.sk) krige.conv: model with constant mean krige.conv: Kriging