Here is one solution to what I think you are after. Generate data. myData <- mapply(rnorm, 1000, 200, mean=seq(-50,50,0.5)) This is a matrix with 1000 rows (observations) and 201 time points. In each time point the mean of data there shifts gradually from -50 to 50. By 0.5 each time....

This looks like a bad evaluation situation in geoR. I mean, a bug! If you rename your c object to something else, it works: ccc =c q <- ksline(ccc, cov.model=fitted_model$cov.model, cov.pars=fitted_model$cov.pars, nugget=fitted_model$nugget, locations=in_mat) image(q) # now works This would be because image.kriging is trying to get something from the original...

Your problem is not a software problem. It's rather a mathematical one. Your first data contains the two following points (0 , 2) and (1e-7, 2) that are very very close but correspond to (very) different outputs: 4 and 5. Therefore, you are trying to adjust a Kriging model, which...