kriging
hi Tom, That was my impression from reading some introductory texts... I'll have to see how the mgcv package fits the polynomial function to the data...it isn't clear to me at first glance how it is accomplished. Many thanks for your advice. Dave
Tomislav Hengl wrote:
Dear Dave, I separate fitting of the deterministic (trend) and residual part of the universal kriging model all the time. Adding OK of residuals to the trend is fine, as long as the regression model is estimated using GLS (but many do it even if they use only OLS; the difference is often minor). Both KED and RK give the same results, but in order to run KED, you need to have the residuals, so you always have to model the trend-part anyway. See some code at: https://stat.ethz.ch/pipermail/r-sig-geo/2008-April/003433.html In future, please keep the whole history of correspondence. Tom Hengl http://spatial-analyst.net -----Original Message----- From: Dave Depew [mailto:ddepew at sciborg.uwaterloo.ca] Sent: dinsdag 17 juni 2008 14:57 To: T.Hengl at uva.nl Cc: r-sig-geo at stat.math.ethz.ch Subject: re:kriging Thanks Tom, I've been able to fit a polynomial function to the data quite well. The residuals are behaving (i.e normal distribution and no skewness of variance). I'm assuming this means that I could krige the residuals (Ordinary K?) and then add the trend back to the predicted residual grid? I realize that I won't be able to place confidence limits on the predictions, but the data is primarily to show that we might be able to use GPS hydroacoustic signals to show macrophyte cover and estimate the standing crop. I'm still a newbie when it comes to the theory involved in kriging, but I think I am familiar with the basics...the variogram of the residuals is a nice spherical model (i.e. it reaches a sill about 50% or so above the nugget, and there is little scatter). I am assuming (perhaps wrongly) that the residuals may be modelled with a variogram and then kriged...