similar to: What value to put in range when I make kriging interpolation?

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

Juan Zwolinski wrote: > > 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) > >

Hello! Here is my problem, I have got a data set that contains values of intensity over a regular interval of time. What I need to do is to resample this data set (or curve) at a different interval of time x (1000 predictions will be made to resample the curve). So in order to perform such an operation I am required to do some kind of interpolation. I know that through kriging, the

All, I am new to using R and know some basics. I wish to use kriging in R to do the following: given data Y =f(X1,X2,X3,.....,Xn) --1000+ irregular measured data set. I would like to be able to get a single value y given sinle input set (x1,x2,x3,...xn) A google search on this takes me lierally to the same example on involving analysis with soil sampling and I cannot figure out how to

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",

R Users: ...been working with the sp and gstat packages for the past couple of days in an effort to analyze a set of ~ 200 soil samples collected from various eastings, northings, and depths and containing a wide range of measured hydrocarbon concentrations. Thus far, I've managed to import the data, log-transform the concentrations, assign coordinates, generate and fit a variogram model and

Hi, I am new in R and trying to implement an algorithm which makes ordinary kriging by using gstat library and the predict method. I use the predict method as following: First, I create an object g: g <- gstat(id="tec", formula=TEC ~ 1, data=data) ## Create gstat object called g And then I use this object in the predict. p <- predict.gstat(g, model=mod, newdata=predGrid,

Hi If you read the description of "varcov.spatial" you'll see that it is used to *predict* a covariance matrix, based on the parameters of the covariance function. So you don't need the oebserved data, you need parameters for the covariance function. Regards EJ On Thu, 2003-04-17 at 05:43, Yan Yu wrote: > THanks for the suggestion. > I have a Q when trying to use it..

Hi All, I am a PhD student in forestry science and I am working with LiDAR data set (huge data set). I am a brand-new in R and geostatistic (SORRY, my background it?s in forestry) but I wish improve my skill for improve myself. I wish to develop a methodology to processing a large data-set of points (typical in LiDAR) but there is a problem with memory. I had created a subsample data-base but

Hello again, There is any package which does Z aware (real 3D) interpolations? It can be any method (IDW, kriging or spline) but it should take into consideration not only x and y coordinates for interpolation, but also z coordinate. I looked into different packages but it seems i didn't find the right one. The ultimate goal is to import the output into a GIS (Geographical Information

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

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

Hello to everybody ! I don't have a question directly connected with R, but it takes starting point from the use of the R function Krig for kriging interpolation and from the idea of making a different implemetation of the function for my purposes. Doas anyone know where can I find free C or C++ codes of kriging ? As always, thanks a lot in advance, Paola.

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 Aaron, I am really a tool user and not a tool maker (actually an ecologist doing some biostatistics)... so, I take the liberty of sending a copy of this e-mail to the r-help list where capable computer persons and true statisticians may provide more relevant information and also to Paulo Ribeiro and Peter Diggle, the authors of geoR.. I really feel that your huge matrix cannot be

The majority of the functionality present in the gstat stand-alone program (http://www.gstat.org/) is now available as a package/library for the S language (R, S-Plus), again called gstat. The package provides multivariable geostatistical modelling, prediction and simulation, as well as several visualisation functions. Gstat was started 10 years ago and was released under the GPL in 1996; the

The majority of the functionality present in the gstat stand-alone program (http://www.gstat.org/) is now available as a package/library for the S language (R, S-Plus), again called gstat. The package provides multivariable geostatistical modelling, prediction and simulation, as well as several visualisation functions. Gstat was started 10 years ago and was released under the GPL in 1996; the

Hello R users, I have numerical data sampled on two grids, each one shifted by 0.5 from the other. For example: grid1 = expand.grid(x=0:3, y=0.5:2.5) grid2 = expand.grid(x=0.5:2.5, y=0:3) gridFinal = expand.grid(x=0.5:2.5, y=0.5:2.5) plot(gridFinal, xlim=c(0,3), ylim=c(0,3), col="black", pch=19) points(grid1, xlim=c(0,3), ylim=c(0,3), col="red", pch=19) points(grid2,

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! Im new to R and I need to interpolate a shapefile using kriging. I've been able to plot/read the shapefile using the package maptools or rgdal. I've searched the internet for sample codes but most of the kriging codes that I've found done in R is done using txtfiles or CSVs.  An example could be of great help. Thanks. [[alternative HTML version deleted]]