search for: semivariogram

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 of model semivariograms. I guess that the inverse of some matrix need to be calculated , and this matri...

I need the most straightforward way to build semivariograms within R i am currently using s-gems software but i would like to even test R Thanks D [[alternative HTML version deleted]]

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) dev.copy2eps(file="fig2.eps",horizontal=T) dev.off() R complains that Error in title(main = "Semivariog...

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 0.00000 2 Exp 3.476700 29.70914 This is the output of v.fit. Can any...

...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 the semivariogram is periodic shape and I am not to able to try a fit the model. This is a maximum of two weeks of work (day bay day) SORRY. Is there a geostatistical user I am very happy to listen his suggests. Data format is X, Y and Z (height to create the DEM) in txt format I have this questions: 1. A...

...sistema es computacionalmente singular: n?mero de condici?n rec?proco = 1.05091e-159 I also wander if there is any limit for kappa. Which one would be the maximun. As far as I'm concerned, it must be always major than cero, right?. I also know that its values depend on the tendency of cloud semivariogram, if it flattens slowly (Gaussian) it's major. I'll thank the one available to answer my desperate question.

...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 semivariogram spherical by groping: x<-ts(rnorm(73)) cs1<-corSpher(form=~x) y<-Variogram(cs1,2628) #2628 corresponding to the " distance" described in l' helps, that is to say the number of station* (the number of station -1) /2. But there does not go it misses apparently much d'...

Is there anyway to change any y[i] value (i=2,...6) to make following NLS workable? x <- c(0,5,10,15,20,25,30) y <- c(1.00000,0.82000,0.68000,0.64000,0.66667,0.68667,0.64000) lm(1/y ~~ x) nls(1/y ~~ a+b*x^c, start=list(a=1.16122,b=0.01565,c=1), trace=TRUE) #0.0920573 : 1.16122 0.01565 1.00000 #Error in numericDeriv(form[[3]], names(ind), env) : # Missing value or

A majority of my data makes a kriged map perfectly using an exponential model for the semivariogram to fit my data and then going through the commands variofit() to define the model and then krige.conv() to use the model to predict values in a grid. But?one set of my data appears to be linearly correlated for the first 5000 meters and not correlated beyond that. I have been having problems u...

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Hello All! I like to do some indicator kriging with R. So far I used geoR for simple and ordinary kriging. Does anybody know which package I should use? Thanks for your help! Sascha! -- Jetzt dabei sein: http://www.shortview.de/wasistshortview.php?mc=sv_ext_mf at gmx

Dear list members, I have a large dataset with point data. Each data point has X, Y, number of species present and values for one single explanatory variable. Each point was generated from a polygon grid shapefile and they are at equal distances. I conducted GLS analyses introducing variance structures and correlation structures to improve the model. I plotted variograms before and after adding

...g into my model. I am trying to model the presence of dives (versus simulated pseudo-absences) using a binomial generalized binomial model (glmmPQL), since I can incorporate the autocorrelation structure to the model using that package. However, I'm trying to come up with ways to evaluate which semivariogram is the most adequate for my model, but not having AICs complicated things a bit for me. Any suggestions on how to best achieve this? Thanks in advance ---------------------------------------- Luis A. Huckstadt, Ph.D. Department of Ecology and Evolutionary Biology University of California Santa Cru...

Hi R users! I will try to state my question again. I have longitudinal data and fitted the following model with lme: Y = X*beta + U + W(t) + Z where U ~ N(0, nu*I) I is the identity matrix, so this is the random intercept W(t)~ N(0, sigma*H) and H is a matrix which incorporates a Gaussian serial correlation (covariance) in the offdiagonal

Hi all- this seems to be simple to figure out but since im new to writing functions I dont know what is happening. Here is my code along with the error I am receiving: semivario=function(data,ids,times,resids){ id=unique(data$ids) index=combinations(length(data$times[data$ids==id[1]]),2) time=gamma=numeric(dim(index)[1]) for (j in 1:dim(index)[1]){