lme fits using (restricted) maximum likelihood, and that requires a covariance/correlation matrix that is non-singular, to be inverted each iteration.
Under spatial correlation models, even the pure nugget model, observations with identical location are perfectly correlated (as in: the correlation of X with itself), and so result in duplicate rows/cols, making the covariance/correlation matrix singular. On 10/31/2010 11:21 AM, valerio.bartol...@uniroma1.it wrote: > Dear list, > it is probably a very simple question with an obvious answer, that > unfortunately I cannot find by myself. > > Why I cannot fit a spatial correlation structure model if I've some > observations in the same location? Shouldn't the nugget account exactly for > this small-scale variability and measurement errors? > > I'm using one of the correlation models in the generic function 'lme' > (package:nlme) > > Thank you > > Valerio > -- Edzer Pebesma Institute for Geoinformatics (ifgi), University of Münster Weseler Straße 253, 48151 Münster, Germany. Phone: +49 251 8333081, Fax: +49 251 8339763 http://ifgi.uni-muenster.de http://www.52north.org/geostatistics e.pebe...@wwu.de _______________________________________________ R-sig-Geo mailing list R-sig-Geo@stat.math.ethz.ch https://stat.ethz.ch/mailman/listinfo/r-sig-geo
