| xvalid {geoR} | R Documentation |
A function to perform model validation by comparing observed and values predicted by kriging. Options include: (i) 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 a given model. This can be computed for all or a subset of the data locations; (ii) external validation can be performed by using validation locations other than data locations.
xvalid(geodata, coords = geodata$coords, data = geodata$data,
model, reestimate = FALSE, variog.obj = NULL,
output.reestimate = FALSE, locations.xvalid = "all",
data.xvalid = NULL, messages, ...)
geodata |
a list containing element coords
as described next. Typically an object of the class
"geodata" - a geoR data-set.
If not provided the arguments
coords must be provided instead. |
coords |
an n x 2 matrix containing
coordinates of the n data locations in each row.
Defaults to geodata$coords, if provided. |
data |
a vector or matrix with data values.
If a matrix is provided, each column is regarded as one variable or realization.
Defaults to geodata$data, if provided. |
model |
an object containing information on a fitted
model. Typically an output of likfit,
variofit. If an object of the class
eyefit is passed it takes the first model
specified in the object. |
reestimate |
logical. Indicates whether or not the model parameters should be re-estimated for each point removed from the data-set. |
variog.obj |
on object with the empirical variogram, typically an
output of the function variog. Only used if
reestimate = TRUE and the object passed to the argument
model is the result of a variogram based estimation, i.e. if
the model was fitted by variofit. |
output.reestimate |
logical. Only valid if reestimate =
TRUE. Specifies whether the re-estimated parameters are returned. |
locations.xvalid |
there are three possible specifications for
this argument: "all" indicates the leaving-on-out
method is used at all data locations.
The second possibility is to use only a sub-set of
the data for cross-validation in which case the argument takes
a vector with numbers (indexes) indicating at which of the data locations the
cross-validation should be performed. The third option is to perform
external validation, on locations other than data locations used for
the model. For the latter a matrix with the
coordinates of the validation points should be provided and the
argument data.xvalid mandatory. |
data.xvalid |
data values at the validation locations. Only used if the validation locations are other than the data locations. |
messages |
logical. Indicates whether status messages should be printed on the screen (or output device) while the function is running. |
... |
further arguments to the minimization functions used by
likfit, variofit. |
The cross-validation uses internally 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 when
reestimating the model.
See documentation of the function likfit for further
details on the model specification and 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 |
the differences data - predicted value. |
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. |
A method for summary returns summary statistics for the errors
and standard errors.
If reestimate = TRUE and output = TRUE additional
columns are added to the resulting data-frame with the
values of the re-estimated parameters.
Paulo J. Ribeiro Jr. paulojus@ufpr.br,
Peter J. Diggle p.diggle@lancaster.ac.uk.
Further information on the package geoR can be found at:
http://www.leg.ufpr.br/geoR.
plot.xvalid for plotting of the results, likfit,
variofit for parameter estimation and
krige.conv for the kriging method used for predictions.
# # 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 without reestimating the model # s100.xv.ml <- xvalid(s100, model = s100.ml) s100.xv.wls <- xvalid(s100, model = s100.wls) ## ## Plotting results ## par.ori <- par(no.readonly = TRUE) ## par(mfcol=c(5,2), mar=c(2.3,2.3,.5,.5), mgp=c(1.3, .6, 0)) plot(s100.xv.ml) par(mfcol=c(5,2)) plot(s100.xv.wls) ## par(par.ori) #