similar to: non-stationary covariance

I am experiencing strange results using dmvnorm. I define a scaled distance matrix from the coordinates bellow and then calculate a covariance matrix using a spherical correlation function. Then with certain combinations of range and sill parameters dmvnorm is returning values greater than 1. Surely the results of dmvnorm should be in the interval 0:1 (or do I just nead a holiday?). In addition

Dear friends, When i read the R-help , i often find the following case: e.g.:surf.gls(np, covmod, x, y, z, nx = 1000, ...) #Package Spatial: Arguments: covmod: function to evaluate covariance or correlation function * ...: parameters for 'covmod'* How should i find the further information on "*...: parameters for 'covmod'*" ? I use the ?covmod, but

Hello to everybody ! I don't have a question directly connected with R, but it takes starting point from the use of the R function Krig for kriging interpolation and from the idea of making a different implemetation of the function for my purposes. Doas anyone know where can I find free C or C++ codes of kriging ? As always, thanks a lot in advance, Paola.

Hi, I have a 4 column file (long/lat/elev/variable) and I tried to fit the values of my variable to the XYZ space using Tps and I keep getting the following message: Warning messages: 1: GCV search gives a minumum at the endpoints of the grid search in: Krig.find.gcvmin(info, lambda.grid, gcv.grid$GCV, Krig.fgcv, 2: GCV search gives a minumum at the endpoints of the grid search in:

hello I try to build DEM using Krig function of fields package. And I get this error message. here is the procedure I followed: > dt<-read.table("/usr/local/bartin/stat/topostat1",header=F,sep="|") > names(dt) <-c("x","y","z") > coord<-cbind(dt$x,dt$y) > elevation<-cbind(dt$z) >

Dear R-experts, I have a question on the formulas used in the gam function of the mgcv package. I am trying to understand the relationships between: y~s(x1)+s(x2)+s(x3)+s(x4) and y~s(x1,x2,x3,x4) Does the latter contain the former? what about the smoothers of all interaction terms? I have (tried to) read the manual pages of gam, formula.gam, smooth.terms, linear.functional.terms but

Dear R-experts, I have a question on the formulas used in the gam function of the mgcv package. I am trying to understand the relationships between: y~s(x1)+s(x2)+s(x3)+s(x4) and y~s(x1,x2,x3,x4) Does the latter contain the former? what about the smoothers of all interaction terms? I have (tried to) read the manual pages of gam, formula.gam, smooth.terms, linear.functional.terms but

Dear R-helpers! What it's means "Rank failure in Choleski decomposition" by using of function surf.gls {spatial}? Sincerely Yours Branimir K. Hackenberger

Hello! I am writing you because I could not plot the confidence envelopes for functions Jest, Jcross, Jdot, Jmulti, and L, using the Spatstat package. I have already understood how to do that for Kest or Jest, that is: JEnv <- plot(envelope(PPPData, Jest)) Where PPPData is my ppp object. However, for Jcross I must specify the two marks I want to analyse. That is, usually I would get the

Hello, there, I got this error when i tried to run " data.kr <- surf.gls(2, expcov, data, d=0.7);" "Error: cannot allocate vector of size 382890 Kb Execution halted" My data is 100x100 grid. the following is the summary of "data": > summary(data); x y z Min. : 1.00 Min. : 1.00 Min. :-1.0172 1st Qu.: 26.00

> -----Original Message----- > From: r-help-bounces at stat.math.ethz.ch > [mailto:r-help-bounces at stat.math.ethz.ch]On Behalf Of Mike J Smith > Sent: 09 June 2005 09:58 > To: r-help at stat.math.ethz.ch > Subject: [R] krig.image help > > > Hi > > I have recently been experimenting with the use of kriging, primarily > through Goldensoftware's Surfer.

Am Dienstag, 25. April 2017, 08:50:34 schrieb Dirk Eddelbuettel: > On 25 April 2017 at 14:58, G?ran Brostr?m wrote: > | hello, > | > | I just installed R-3.4.0 from scratch: > | > | $ sudo apt install r-base > | > | but when I try > | > | > library(survival, lib.loc = "/usr/lib/R/library") > | > fit <- coxph(Surv(exit, event) ~ x, data =

Full_Name: cajo ter Braak Version: 2.1.1 OS: Windows Submission from: (NULL) (137.224.10.105) # I would like to attach the matrix C in the Rdata file; it is 50x50 and comes from a geostatistical problem (spherical covariogram) > rm(list=ls(all=TRUE)) > load(file= "test.eigen.Rdata") > ls() [1] "C" "eW" > > sym.check = max(abs(C - t(C))) # should

Thanks for all the helpful responses. I include the data file and the syntax file for reference. Again, if I use the fields function, as is, I get the message: Error in svd(tempM) : error 159 in dsvdc using traceback, I get: > traceback() 4: stop(paste("error ", z$info, " in dsvdc")) 3: svd(tempM) 2: Krig(x, Y, cov.function = rad.cov, m = m, decomp = decomp,

Hi, I am trying to do a bayesian prediction for soil pollution data above a certain threshold, using geoR. Everything is working fine until i am doing the krig.bayes. I tried to do the prediction on a grid 67 by 113 cells and my computer is freezing to death. At larger numbers of cells it tells me after a while that it reaches the max. memory of 511 Mb. My computer has only 512 Mb of RAM.

Our webserver is configured as such: The actual website files, php, html ,css and so on. Or on a dedicated non-glusterfs ext4 partition. However, the website access Videos and especially image files on a gluster mounted directory. The write performance for our backend gluster storage is not that important. Since it only comes into play when someone uploads a video or image. However, the files

Hi list, Well, this time I’ve a doubt with mapping generation. I was already able to read and plot shapefiles, plot point on this map. All this without any problems, but now I want to do something that I think, only Golden Software Surfer is capable of. I would like to plot a kriging result on the existing map (map script below). Well I looked for on the net, but I was not able to be sure of

R Experts, I'm looking for some help with the geoR package. I'm trying to krig some data without using a global neighborhood. I would like to set my moving neighborhood to a distance, say 100 meters, where I know my data is spatially correlated. I have tried the ksline function, but that only allows my moving neighborhood to be set to a number of data points. But, since my data

Hi R-people: Working in geoRglm, it shows me, according to AIC criterion, that the non-spacial model describes the process in a better way. It's the first time that I'm facing up to. These are my results: OP2003Seppos.AICnonsp-OP2003Seppos.AICsp #[1] -4 (OP2003Seppos.lf0.p<-exp(OP2003Seppos.lf0$beta)/(1+exp(OP2003Seppos.lf0$beta))) #P non spatial #[1] 0.9717596

Hello, I am trying to estimate a multivariate regression of Y on X with regression splines. Y is (nx1), and X is (nxd), with d>1. I assume the data is generated by some unknown regression function f(X), as in Y = f(X) + u, where u is some well-behaved regression error. I want to estimate f(X) via regression splines (tensor product splines). Then, I want to get the predicted values for some new