similar to: Spatial regression

Hello. I'm pretty much new to R and I'm trying to produce some figures. It seems to me, that R has some asynchronous way of plotting figures. When I run this code: #constructs the semivariogram of SC1929 vgm1 <- variogram(SC1929~1,~U+V,puerto.map$att.data) # trying to make new plot dev.set(which=dev.next()) plot(vgm1) title(main="Semivariogram",font.main=4)

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

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, 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

Hello, can any one point me to R-packages (if available) which include spatial Markov Chain methods? My second question is more general but hopefully not OT: Currently we are using the software TPROGS, which let people simulate property distributions in space by some Markov Chain approaches. We face some problems due to the lack of information between distances of samples along borehole path

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

Hi; I cannot find in the R html documentation a way to evaluate cross-correlation in 2D data sets. I would like to evaluate cross-correlation in a series of moving windows between two maps. i,e, specify several windows inside the complete 2D spatial matrixes and for each one ofthese windows evaluate the 2D cross-correlation (commonly conducted in the spectral domain). Thanks in advance and best

Dear R users, I am putting together reading and resources lists for spatial statistics and spatial econometrics and am looking for some pointers from more experienced practitioners. In particular, I find two "camps" in spatial modelling, and am wondering which approach is better suitied to which situation. The first camp is along the lines of Venables and Ripley's Chapter 14

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

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

Hi, I would know how to plot two variograms on a same graph. I can plot one by one but I would draw both on the same one. Is it possible? Do i need any special package? Thanks! Cordialement Damien Landais

Hi, R-folks: I have been tryin many combination of parameter to make Matern variogram to work, but I can't find the available one. I'm near to be crazy. I tiped: A?o2003Selg.lf<-likfit(A?o2003Selg,cov.model="matern",ini.cov.pars=c(1.5,14),kappa=2.5,fix.kappa=FALSE,nugget=0.08,lambda=0.008,fix.lambda=FALSE,hessian=TRUE) the hessian shows: $hessian [,1]

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

Hello everyone! I am using GRASS with spgrass6 for my work. I will be using variograms in the process of landsat image classification. I am quite ok with GRASS but am finding R really tough. I understand that spgrass6 is a link between GRASS and R which can read and write raster/vector layers. Out of really many packages in R, for generating variograms out of landsat images which packages of R

Hi, I want to automatically fit variograms to a large number of different sample data sets, and call the funtion "likfit" (in package geoR) from within a for-loop. "likfit" does again call "optim". After ssuccessfully fitting variograms to some of the data sets, the procedure crashes and I get the error message: Error in optim(par = ini, fn = negloglik.GRF,

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

Hello, Could someone tell me whether the spatial statistics module of Splus is worth the rather high price, or whether one could (with reasonable effort) carry out the same things using R and some of its contributed packages. Although we do have Splus (Windows and Unix), we definitely prefer to use R whenever feasible (both for research and teaching). Thanks for developing a superb

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.

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Hello all, I am trying to use "clim.pact" package for my work, but since this is the beginning for me to use gridded datasets in "R", I am having some trouble. I want to do seasonal analyses like trends, anomalies, variograms, EOF and probably kriging too to downscale my 1 degree gridded data to 0.5.  So, as a first step, I compiled my entire dataset (with 25