similar to: VECTOR_ELT() can only be applied to a 'list', not a 'integer (gstat problem)

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

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

Sale plano sí. Ya se que sin tener los datos y el código es un poco difícil, pero es que mis datos ocupan mucho, es imposible. Seguiré mirando por internet. Muchas gracias Rubén. Un saludo, > To: r-help-es en r-project.org > From: rubenfcasal en gmail.com > Date: Thu, 6 Aug 2015 14:21:47 +0200 > Subject: Re: [R-es] Duda interpolación (package ' gstat ') > > Hola

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,

Hola, # Hacemos el KED. Ver función "krige()": KED.rad <- krige( formula=pluvPcp~layer, # covariable -> radar locations=lluvia.rad.pluv.spdf, newdata=radarGrid, # podría ser cualquier objeto Spatial model=v.fit, # modelo de semivariograma. maxdist=Inf

Soon on CRAN a new version of package gstat will emerge, which has a few minor changes and possible incompatibilities w.r.t. the previous version(s). The new gstat (0.9-23) now: + depends on sp, and uses internally with Spatial* classes from sp if data are provided in the old-fashoned way (as data.frame) + has a vignette to get you started with the classes in sp + defines krige as a generic;

Soon on CRAN a new version of package gstat will emerge, which has a few minor changes and possible incompatibilities w.r.t. the previous version(s). The new gstat (0.9-23) now: + depends on sp, and uses internally with Spatial* classes from sp if data are provided in the old-fashoned way (as data.frame) + has a vignette to get you started with the classes in sp + defines krige as a generic;

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

Hello, I'm using the gstat package within R for an automated procedure that uses ordinary kriging. I can see that there is a logical ("singular") atrtibute of some adjusted model semivariograms: .- attr(*, "singular")= logi TRUE I cannot find documentation about the exact meaning and the implications of this attribute, and I dont know anything about the inner calculations

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

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

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, please help!! I've been trying to fit variogram models using the REML method in the gstat package. Every time the Windows GUI crashes. For example library(gstat) data(meuse) x <- variogram(zinc ~ 1, ~x + y, meuse) v <- vgm(140000, "Sph", 800, nug = 10000) plot(x, model = fit.variogram(x, model = v, fit.method=5)) Other fit methods are non problematic (eg. fit.method=7

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, 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

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

Hi all, >str(df) `data.frame': 31837 obs. of 3 variables: $ x : num 410683 410700 410720 410740 410324 ... $ y : num 43136 43126 43123 43125 42709 ... $ wz: num -101.1 -94.9 -93.3 -94.5 30.8 ... >library(gstat) >g<-gstat(id="rv",form=wz~1,loc=~x+y,data=df,model=mat,nmax=500,set=list(average=1)) >str(g) List of 3 $ data :List of 1 ..$ rv:List of 10 ..

Dear All, I run following code to estimate the blocks using cokriging ( my data set has more than 50,000 data points). All the things run finely but Once I run the predict.gstat function it gave the error message - "memory.c", line 58: can't allocate memory in function m_get(). I run this code on LINUX sever but result is same. Would any one please be able to give a solution for