| xvalid {geoR} | R Documentation |
This is a function to perform model validation. Options include leaving-one-out cross-validation where. each data location is removed from the data set and the variable at this location is predicted using the remaining locations, for as given model. This can be done for all or some of the locations. Alternativelly, other validation locations which are not the same as the original data locations can be used.
xvalid(geodata, coords = geodata$coords, data = geodata$data,
model, reestimate = FALSE, variog.obj = NULL,
output.reestimate = FALSE, locations.xvalid = "all",
data.xvalid = NULL, messages.screen = TRUE, ...)
The cross-validation uses the function krige.conv to predict
at each location.
For models fitted by variofit the
parameters kappa, psiA, psiR
and lambda are always regarded as fixed.
See documentation of the function likfit for more
details on the model and its parameters.
An object of the class "xvalid" which is a list with the following components:
data |
the original data. |
predicted |
the values predicted by cross-validation. |
krige.var |
the cross-validation prediction variance. |
error |
difference data - predicted. |
std.error |
the errors divided by the square root of the prediction variances. |
prob |
the cumulative probability at original value under a normal distribution with parameters given by the cross-validation results. |
If reestimate = TRUE and output = TRUE additional
columns are added to the data-frame. Each column will contain the
values of the re-estimated parameters.
Paulo J. Ribeiro Jr. Paulo.Ribeiro@est.ufpr.br,
Peter J. Diggle p.diggle@lancaster.ac.uk.
Further information about geoR can be found at:
http://www.maths.lancs.ac.uk/~ribeiro/geoR.html.
plot.xvalid for plotting of the results, likfit,
variofit for parameter estimation and
krige.conv for the kriging method used for predictions.
if(is.R()) data(s100) # # Maximum likelihood estimation # s100.ml <- likfit(s100, ini = c(.5, .5), fix.nug = TRUE) # # Weighted least squares estimation # s100.var <- variog(s100, max.dist = 1) s100.wls <- variofit(s100.var, ini = c(.5, .5), fix.nug = TRUE) # # Now, performing cross-validation # s100.xv.ml <- xvalid(s100, model = s100.ml) s100.xv.wls <- xvalid(s100, model = s100.wls)