similar to: kriging with geoR package

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) >

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

Hi, i get factors from oracle instead real or numeric, but have problems to convert. Where is my mistake? I can't rember have this difficulties like this? many thanks, christian > vsub <- subset(dm,select=c(DEBITS_POST3)) > (vsub$DEBITS_POST3[1:4]) [1] 9,93 0 23,82 32,72 7936 Levels: 0 0,53 0,65 0,73 0,76 1,02 1,03 1,16 1,25 1,28 1,31 1,38 1,45 1,47 1,49 1,52 1,53 1,62 1,68

Estimados quería realizarles una consulta: Trabajo con una tabla con 23 registros, la cual tiene en la columna "*ar4*" valores de una variable llamada ranking para cada registro, y una columna "percentil" donde le voy a colocar el nivel de percentil en el que se encuentra ese valor (ar4) de cada uno de esos 23 registros. La tabla es la siguiente:: código padre n ar4

I have been modelling spatial data using function likfit in package geoR. Now that I have the spatial regression models, I would like to calculate the amount of variance explained by both the trend and the spatial parts of the model. Any advice on how to do this would be much appreciated. Cheers, Ailsa

Hi everyone !   I'm am trying to fit a kriging model to a set of data. When I just run the "likfit" command I can obtain the results. However when I try to pass additional arguements to the optimization function "optim" I get errors. That is I want to obtain the hessian matrix so matrix (hessian=TRUE).   Heres a little example( 1-D). Can anyone shed some light?  Where am I

Dear all, I'm trying to fit a variogram model using variofit function in geoR package. There is an option to fix the nugget but is there anyway to force the variogram sill to equal some defined value? I'm working with standardized longitudinal data so the process variance is 1 and I'd like to force the sill of the variogram to reach this value (and prevent it from going

Dear, we have a problem with geoR. We try to read an ASCII table (x,y,z) with 40000 lines. With read.geodata we get the error: Error in vector("double", length) : cannot allocate vector of length 799980000 We can read the file without any problem with read.table, but trying to convert it to the geodata class gets the same error. Any help/tips would be appreciated. Kris, Dave --

I used points graphic (geoR) in this way: points(zn,pt.divide=c("quintiles"), main="Location map of Zn",lambda=1,col="gray",yl="coord y",xl="coord x",x.leg=0. 3, y.leg=5.5,dig.leg=2,cex.min=0.5,cex.max=1,) Can I reduce legend characters (a sort of cex)?

Greetings comRades: Has anyone worked out a procedure to calculate the estimated covariance between the beta and lambda estimates in the likelihood-based geostatistical model implemented in package geoR? If so, please help me out by letting me know. Do I have reasons to expect that this covariance term would be big or perhaps more happily, that it could be discarded without much effect?

Dear GeoR-er, If I use the variog function in the latest release of geoR, the first lag is always ignored. For instance, if you read in geodata, calculates the variogram using the variog function and give in a uvec like uvec=seq(0,max,by=2.44), it only starts giving results from distance=4.88 and ignores 2.44! This wasn't the case in former versions of geoR. Is this done on purpose? Why?

I've enconuntered this problem with the last cran version of geoR: > library(geoR) > day <- rep(1:2, each=5) > coords <- matrix(rep(runif(10),2), 10, 2) > data <- rnorm(10) > data[1] <- NA > as.geodata(cbind(coords, data, day), realisations=4) as.geodata: 1 points removed due to NA in the data Errore in as.geodata(cbind(coords, data, day), realisations = 4) :

Hi cross posting from sig geo , because I really need help! I'm using geoR for some spatial linear models and I'm getting surprisingly optimistic values from the spatial models relative to the non-spatial, even when the models appear to be performing about equally (by AIC comparison) For example This model relating encounter rates of lizards to a soil substrate parameter gives >

Mzabalazo Ngwenya wrote: > Hi everyone ! > > I'm am trying to fit a kriging model to a set of data. When I just run > the "likfit" command I can obtain the results. However when I try to > pass additional arguements to the optimization function "optim" I get > errors. That is I want to obtain the hessian matrix so matrix > (hessian=TRUE). >

I am having difficulty getting the trend.spatial function in geoR to work properly. After creating a trend.spatial object with a covariate, I try to add the command into my likfit() function as follows: trend1.trend.spatial <- trend.spatial("1st", trend1.geodata) trend1.spatial.EC0.1.reml <- likfit(spatial.geodata, trend1.trend.spatial, ini.cov.pars = spatial.EC0.1.eyefit,

Hello All! I calculate a variogram using the function variog (package geoR) afterwards I use variofit to fit a spherical model (see code below). Now I just changed the units of the variable (in this case MPa to kPa just a factor of 1000). If I do so, I get a different fit and therefore different ranges etc. Why? The semi-variance is of course 6 orders of magnitude higher but the values

Hi I'm using geoR package to perform linear spatial interpolation(OK). The function likfit() fails to compute REML. The error meassage is : Error in solve.default(v$varcov, xmat); How I can find out that likfit() is failed to process and retrieving the error message ? Thank you so much for your help.

Mac OS 10.3.5, R 2.0.0 latest version of geoR I have an incomplete 5 x 20 spatial array of samples (60 out of 100 possible locations) for which I would like to calculate directional variograms using variog4(). Unfortunately, I can't get it to return all 4 directions. It returns variograms for 45, 90, and 135 degrees, omitting 0 degrees (pi/4, pi/2, 3pi/4, omitting 0). If I specify 0

Hi, Im using geoR and I'm trying to do some predictions, based on an external trend. I'm having some problems specifying my model.control, specifically how do I define my model, and also the source of the covariate data at the prediction locations? I am assuming that the covariate data at the prediction locations should be imported to a geodata object along with the prediction location

Hello, I am attempting to get the standard deviation in multiple distance bins in my spatial data. It appears as though the 'variog' command in the geoR package will do the trick, as one of the outputs from 'variog' is 'variog$sd', which, according to the manual, is the "standard deviation of the values in each bin". However, when I run this command, the