search for: variofit

dear list I am student M.S. statistics in department statistics . I am working in the function "nls" in the [R 2.3.1] with 246 data and want to fit the "exp" model to vectors( v and u ) but I have a problem to use it u 5.000000e-13 2.179057e+03 6.537171e+03 1.089529e+04 1.525340e+04 1.961151e+04 2.396963e+04 2.832774e+04 3.268586e+04 3.704397e+04 4.140209e+04

Hello All! I calculate a variogram using the function variog (package geoR) afterwards I use variofit to fit a spherical model (see code below). Now I just changed the units of the variable (in this case MPa to kPa just a factor of 1000). If I do so, I get a different fit and therefore different ranges etc. Why? The semi-variance is of course 6 orders of magnitude higher but the values are...

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 using krige.conv() to get a decent kriged map using the linea...

Hi all, I tried to produce some kriged surfaces with geoR (latest version). The size of the grid should be around 900 x 650 cells (what I find is not a very big grid), and the number of points is around 2500. The command krige.conv stopped after arround 5 min saying it can not allocate a vector with around 1.5 billion units. Sounds reasonable. Is there a workaround? How would I partition the

...) ppt2[,1] <- lat[ll] # y-axis ppt2[,2] <- lon[ll] # x-axis ppt2[,3] <- ptemp[ll] # ppt pptd <- as.geodata(ppt2) bin1 <- variog(pptd) # plot(bin1) # fig1 bin2 <- variog(pptd,estimator.type="modulus")# plot(bin2) # fig2 ini1 <- max(bin1$v) ols <- variofit(bin1, fix.nugget = F,weights="cressie",ini.cov.pars=c(ini1,4)) kc <- krige.conv(pptd, loc=pgrid,krige=krige.control(type.krige="OK",trend.d="2nd",trend.l="2nd",cov.pars=ols[2]$cov.pars)) pvalxx <- which(kc$predict < 0) kc$predict[pvalxx] <- 0...

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

...istance matrix yourself from the coordinates file (but you may meet meory problem anyway). Still more straigthfully, if you intend to use interpolation methods such as kriging, you don't need to manage the distance matrix building by yourself. See: library(geoR) library(help=geoR) ?variog ?variofit ?likfit? etc... If you want further use geostatics (eg via geoR or gstat), you will anyway have to manage with memory limits (not due to R). On my computer (portable HP compaq nx7000) I can hardly manage with more than 2000 observations using geoR (far from your 18227 observations). I had to...

...my.variogram.model) >ok<-krige.conv(cadiz.geo,data=amostragem.cadiz$dia1.std,locations=as.matrix(cadiz.polygrid[,c(1,2)]),krige=krige.control.ok) krige.conv: model with constant mean krige.conv: Kriging performed using global neighbourhood where my.variogram.model is gaussian fitted through variofit with "cressie" weigths. I'm getting the same predicted values for both models, 12% of them being negative. Is it because simple and ordinary kriging predictions are not including the overall mean? If this is the case what mean should I use? -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-....

...>ok<-krige.conv(cadiz.geo,data=amostragem.cadiz$dia1.std,locations=as.matrix(cadiz.polygrid[,c(1,2)]),krige=krige.control.ok) > krige.conv: model with constant mean > krige.conv: Kriging performed using global neighbourhood > > where my.variogram.model is gaussian fitted through variofit with > "cressie" weigths. > > I'm getting the same predicted values for both models, 12% of them > being negative. Is it because simple and ordinary kriging predictions > are not including the overall mean? If this is the case what mean should > I use? Thank you f...

Dear all, I'm trying to fit a variogram model using variofit function in geoR package. There is an option to fix the nugget but is there anyway to force the variogram sill to equal some defined value? I'm working with standardized longitudinal data so the process variance is 1 and I'd like to force the sill of the variogram to reach this valu...

Dear List, I have fitted a spherical function to my variogram using "variofit(...)" from GeoR. Now I would like to predict some data with the function "predict(object,...)" from package stats. Does anyone know wether this works and if it does how to do it? Thanks a lot! Anna

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