similar to: LIDAR Problem in R (THANKS for HELP)

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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

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

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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

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

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

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

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

All, I am new to using R and know some basics. I wish to use kriging in R to do the following: given data Y =f(X1,X2,X3,.....,Xn) --1000+ irregular measured data set. I would like to be able to get a single value y given sinle input set (x1,x2,x3,...xn) A google search on this takes me lierally to the same example on involving analysis with soil sampling and I cannot figure out how to

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

Hi ALL, Can you please help me on how to determine the prediction error for ordinary kriging?Below are all the commands i used to generate the OK plot: rsa2 <- readShapeSpatial("residentialsa", CRS("+proj=tmerc +lat_0=39.66666666666666 +lon_0=-8.131906111111112 +k=1 +x_0=0 +y_0=0 +ellps=intl +units=m +no_defs")) x2 <- readShapeSpatial("ptna2",

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

Hello R users, I have numerical data sampled on two grids, each one shifted by 0.5 from the other. For example: grid1 = expand.grid(x=0:3, y=0.5:2.5) grid2 = expand.grid(x=0.5:2.5, y=0:3) gridFinal = expand.grid(x=0.5:2.5, y=0.5:2.5) plot(gridFinal, xlim=c(0,3), ylim=c(0,3), col="black", pch=19) points(grid1, xlim=c(0,3), ylim=c(0,3), col="red", pch=19) points(grid2,

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 ..

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 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

Hello again, There is any package which does Z aware (real 3D) interpolations? It can be any method (IDW, kriging or spline) but it should take into consideration not only x and y coordinates for interpolation, but also z coordinate. I looked into different packages but it seems i didn't find the right one. The ultimate goal is to import the output into a GIS (Geographical Information

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