similar to: Variogram (nlme) of a lme object - corSpatial element question

Dears UseRs, In Zuur's book (Mixed effects models and extensions in ecology with R), p 167, it is mentionned that the Variogram function (package nlme) scale the sill to 1. Here is the plot of the semi-variogram given by the Variogram function on my "lme" object. (http://imm.io/3OLe) Note that the semi-variance exceed 1 !! It can probably explain why the Gaussian correlation

Hello, I am having a problem specifying the correlation structure in lme which leads to the error: Error in getCovariate.corSpatial(object, data = data) : Cannot have zero distances in "corSpatial". I have specified a grouping variable which is the only fix I could find by searching this error on R-help. ISee the below example. When my samples (tran) - which are transects

Hi I'm using R(2.9.0) and gstat package under Windows to plot sample variograms. When I want to fit them, I don't have the possibility to choose Cauchy functions. Under Mathematica for example, we have it and it seems to be the best one to fit the variograms I have I tested the others functions, they can fit but I would,if possible, use the Cauchy one. If anybody knows something about

I wonder what is the default value for the argument 'cutoff' when not specified in the variogram.formula function of gstat. Computing variogram envelops within gstat, I am comparing the results obtained with variog in geoR and variogram in gstat, and it took me a while before understanding that the cutoff default value is not the maximum distance. Can Edzer tell us about it? All the

Dear all, I am aiming to calculate variograms using variogram() from gstat. The problem is that some of my data-sets are very large (> 400000 points). Running the command takes some hours of time and does not give any error-message. Nevertheless the result seem not to be appropriate - the first few bins are ok (up to a distance of about 300) but then it gains lags which are much larger than

Hello R list, I need to compute empirical variograms using data from a large geographic area (~10^6 km2). Although I could not find a specific reference, I assume that both geoR and gstat calculate distances among data points assuming points are on a flat surface (using the Pythagorean Theorem). Because the location of my data is large and located near the pole, assuming that latitude and

I noticed that ?corClasses in package nlme does not list corSpatial among the standard classes. This might either be intentional because corSpatial is not "standard" , or it might be simply an oversight that needs correcting. ------------------------------------------ Robert W. Baer, Ph.D. Professor of Physiology Kirksville College of Osteopathic Medicine A. T. Still University of

Dear Members, I am trying to fit a variogram model using fit.variogram function from the gstat package. The figure showing my experimental variogram can be seen here: https://i.stack.imgur.com/UZXw4.png My code line for this operation is: > c2.vgm.fit<-fit.variogram(c2.vgm.exp,vgm(nugget=0, psill=400,model="Exp",range =40000),fit.method = 7) The system throws following error

Dear All, Is there anybody has the experience in using variogram(gstat) ? Please kindly give me some hints about the results. I used variogram() to build a semivariogram plot as: tr.var=variogram(Incr~1,loc=~X+Y,data=TRI2TU,width=5) then fir the variogram to get the parameters as: v.fit = fit.variogram(tr.var,vgm(0.5,"Exp",300,1)) v.fit model psill range 1 Nug 1.484879

Dear R Helpers I'm investigation the geostatistics tools in R and have found the package 'gstat', which looks to be useful four two dimensional data. However, I usually deal with three dimensional information. I would like to compute a 3D variogram map using the variogram tool but cannot seem to get it to recognise the third (z) dimension The data file reads ok and the spatial

Dear R users, I'm estimating a mixed effects model in which the spatial correlation is controlled for by the "corGaus" structure. I'm wondering if there is a document or paper that explains how the spatial correlation structure (such as "corExp" or "corGaus") works. Let me use the example and data posted on UCLA's R FAQ webpage to explain my problems.

I uploaded gstat 0.9-12 to CRAN, which has a few important changes: 1. Cokriging cross validation Cokriging cross validation is now possible with the function gstat.cv: you simply pass a multivariable gstat object, and cross validation is done for the first variable in the object. Optionally, secondary variable records at locations coinciding with the validation locations are removed. 2. Class

I uploaded gstat 0.9-12 to CRAN, which has a few important changes: 1. Cokriging cross validation Cokriging cross validation is now possible with the function gstat.cv: you simply pass a multivariable gstat object, and cross validation is done for the first variable in the object. Optionally, secondary variable records at locations coinciding with the validation locations are removed. 2. Class

Dear R users, I am a remote sensing researcher currently studying the use of LiDAR data for quantifying tree height, in particular I would like to determine the sample size needed to capture and quantify canopy height variability. To do this I have employed the use of automap(), which automatically calculates variograms including reporting the range of the variogram. The program is easy to use

Hi all-- I'm new to R, statistics and programming, so sorry if this is a really basic question! I have plotted a directional variogram, and I want to a. overlay the omni-directional line over each directional panel b. display the directional variograms in a single panel with a legend that associated each line to each degree measurement. The line I'm using is

Dear R users,, I'm having difficulties when i use the gstat extensions in R especially with the graphical interface, as the variograms plots are depicted with gray background and cyan points for the number of pairs, Consequently the fitted variogram is drawn in cyan, How can i change that so i could have, white background and black lines for the fitted variogram, for the complete plot.

The following came up when Roger Bivand and I discussed R's name spaces and overlap in packages, in the bus to the field trip during StatGIS, last Tuesday: If two packages create the same class, say "variogram", and both are loaded, then using a method for an object of class "variogram" cannot discriminate between them and will call the method in the package loaded last.

Thanks to Edzer and Roger, I can now plot with increased font sizes. However, jpeg still does not reproduce these, nor does it show up in high quality. What I would like to do is produce some highresolution jpegs. Any help would be appreciated Thanx Herry R2.4 on Mandriva 10.2 linux. Dr Alexander Herr Spatial and statistical analyst CSIRO, Sustainable Ecosystems Davies Laboratory, University

Hello, I'm french and I have some difficulties in carry out the semiones under R with an aim to carry out an interpolation by krigeage. My goal is to obtain a chart of the distribution of precipitations/temperatures in Europe starting from 73 different stations (and, of course, distributed irregularly on the chart, where use of the krigeage). Here, I carried out this to test to obtain

Hello Trying to develop variograms and kriged surfaces from a point file. Here is what I've done so far. library(gstat) # also loads library(sp) library(lattice) soilpts$x <- soilpts$UTM_X soilpts$y <- soilpts$UTM_Y soil.dat <- subset(soilpts, select=c(x, y, Area, BulkDensity, LOI, TP, TN, TC, Total_Mg)) dim(soil.dat) [1] 1292 7 coordinates(soil.dat) <- ~ x+y