similar to: krige in gstat() package

ear ALL, The last part of my thesis analysis is the cross validation. Right now I am having difficulty using the cross validation of gstat. Below are my commands with the tsport_ace as the variable: nfold <- 3 part <- sample(1:nfold, 69, replace = TRUE) sel <- (part != 1) m.model <- x2[sel, ] m.valid <- x2[-sel, ] t<- fit.variogram(v,vgm(0.0437, "Exp", 26, 0)) cv69

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

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

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

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,

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

Greetings all, Ran into a strange problem with the krige function from geoR. The problem that I am having is that while the krige function seems to work well, the resulting predicted values are all NAs. Given the size of the datasets I am working with can't attach it, but I can provide snippets of the datasets. > casedata station year month day obs mpe bias type

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Dear all, I'm new on R and this mailing list. We work on spatial rainfall estimation with R and Grass. We have a problem with the krige function from the sgeostat package. We would like to limit the estimated area with a polygon limit. I use a 50 points polygon to describe my work area. The krige function work quiet well without limit. But if I use this option I have the following error

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 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

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

Hello, I'm interested in doing Bayesian kriging using R. I see that the package geoR has a function that will allow one to do this (krige.bayes). However, my data are not in the form of points, but rather they are blocks that represent spatial averages (i.e., the number of fishing hooks per month in a given lat x long square). I am therefore interested in treating the data as

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 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",

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Hello Trying to develop variograms and kriged surfaces from a point file. Here is what I've done so far. library(gstat) # also loads library(sp) library(lattice) soilpts$x <- soilpts$UTM_X soilpts$y <- soilpts$UTM_Y soil.dat <- subset(soilpts, select=c(x, y, Area, BulkDensity, LOI, TP, TN, TC, Total_Mg)) dim(soil.dat) [1] 1292 7 coordinates(soil.dat) <- ~ x+y

Hi everybody, I have a longitude vector and a latitude one. Associated to these coordinates, i have a matrix with some data at some coordinates but not all. Lon <- seq(136.025,144.975,0.05) Lat <- rev(seq(-66.975,-65.525,0.05)) dim(z) <- c(Lon,Lat) And i have tried to apply to these data a kriging function. But first i need to reshape these 3 variables to have a dataframe

Dear all, I would like to estimate rainfall with the krige function. First, I produce a polygon of my region of interest (where poly_test.txt is a x,y suite of points defining a polygon, obtained with the grass v.out.ascii command) : user>border_limite<-read.table("/home/lionel/rwork/poly_test.txt",header=FALSE) user>polygone<-list(x=border_limite[,1],y=border_limite[,2])

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