Dear Users, >From previous analysis (semi-variograms using package gstat), I found spatial autocorrelation in my dataset. The best fitted model to this spatial correlation structure is the Gaussian model (Spherical, Exponential, Linear tested and comparison done by Sum of Square errors). So I used corGaus function to define this spatial autocorrelation in my lme model using the option "correlation".
The Variogram function (package nlme) used on a lme object calculates the semi-variogram for the within-group residuals and add the semi-variogram of the corSpatial element (corGaus in my case) included in my model ... so far no problem. I was surprised, however, to see on the plot of the semi-variogram issued from the Variogram function, (see figure at http://imm.io/3OLe) the low range value (~1600 meters) used in the corGaus structure included in the lme object. When I fitted the same corGaus structure manually or using the fit.variogram function (package gstat) on the data of each group defined in lme, it gaves me ranges between 2050 and 2700 meters (mean 2350 meters). Can anyone explain me those differences ? Note: As I mentioned in a previous message ( http://markmail.org/message/gjgag4ohjopevgax), I tried to define a different range in the corGaus function directly in the lme function, but it is not taken into account. Arnaud [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
