Description Usage Arguments Details Value Author(s) References See Also Examples
View source: R/georob.predict.R
Robust and customary external drift Kriging prediction
based on a spatial linear models fitted by georob
. The
predict
method for the class georob
computes fitted values, point
and block Kriging predictions as
well as model terms for display by termplot
.
1 2 3 4 5 6 7 8 9  ## S3 method for class 'georob'
predict(object, newdata, type = c("signal", "response", "trend", "terms"),
terms = NULL, se.fit = TRUE, signif = 0.95, locations,
variogram.model = NULL, param = NULL, aniso = NULL, variogram.object = NULL,
control = control.predict.georob(), verbose = 0, ...)
control.predict.georob(full.covmat = FALSE, extended.output = FALSE,
mmax = 10000, ncores = pcmp[["max.ncores"]], pwidth = NULL, pheight = NULL,
napp = 1, pcmp = control.pcmp())

object 
an object of class 
newdata 
an optional data frame,

type 
character keyword defining what target quantity should be predicted (computed). Possible values are

terms 
If 
se.fit 
logical, only used if 
signif 
positive numeric equal to the tolerance or confidence level
for computing respective intervals. If 
locations 
an optional onesided formula specifying what variables
of 
variogram.model 
an optional character keyword defining the
variogram model to be used for Kriging, see 
param 
an optional named numeric vector with values of the
variogram parameters used for Kriging, see 
aniso 
an optional named numeric vector with values of anisotropy
parameters of a variogram used for Kriging, see 
variogram.object 
an optional list that defines a possibly nested
variogram model used for Kriging, see 
control 
a list with the components 
full.covmat 
logical controlling whether the full covariance matrix
of the prediction errors is returned ( 
extended.output 
logical controlling whether the covariance
matrices of the Kriging predictions and of the data should be computed, see
Details (default 
mmax 
integer equal to the maximum number (default 
ncores 
positive integer controlling how many cores are used for parallelized computations, see Details. 
pwidth, pheight, napp 
numeric scalars, used to tune numeric
integration of semivariances for block Kriging, see

pcmp 
a list of arguments passed to 
verbose 
positive integer controlling logging of diagnostic
messages to the console. 
... 
arguments passed to 
If newdata
is an object of class SpatialPoints
,
SpatialPixels
or SpatialGrid
then the drift model may only
use the coordinates as covariates (universal Kriging). Furthermore the
names used for the coordinates in newdata
must be the same as in
data
when creating object
(argument locations
of
predict.georob
should not be used). Note that the result returned
by predict.georob
is then an object of class
SpatialPointsDataFrame
, SpatialPixelsDataFrame
or
SpatialGridDataFrame
.
The predict
method for class georob
uses the packages
parallel, snow and snowfall for parallelized
computation of Kriging predictions. If there are m items to
predict, the task is split into ceiling(m/mmax)
subtasks that are
then distributed to ncores
CPUs. Evidently, ncores = 1
suppresses parallel execution. By default, the function uses all
available CPUs as returned by detectCores
.
Note that if full.covmat
is TRUE
mmax
must exceed
m (and parallel execution is not possible).
The argument extended.output = TRUE
is used to compute all
quantities required for (approximately) unbiased backtransformation of
Kriging predictions of logtransformed data to the original scale of the
measurements by lgnpp
. In more detail, the following items
are computed:
trend
: the fitted values,
x(s)^T hatβ,
var.pred
: the variances of the Kriging predictions,
Var[hatY(s)] or
Var[hatS(s)],
cov.pred.target
: the covariances between the predictions and the
prediction targets,
Cov[hatY(s),Y(s)] or
Cov[hatS(s),Z(s)],
var.target
: the variances of the prediction targets
Var[Y(s)] or
Var[Z(s)].
Note that the component var.pred
is also present if
type
is equal to "trend"
, irrespective of the choice for extended.output
.
This component contains then the variances of the fitted values.
If type
is equal to "terms"
then a vector, a matrix, or a
list with prediction results along with bounds and standard errors, see
predict.lm
. Otherwise, the structure and contents
of the output generated by predict.georob
are determined by the
class of newdata
and the logical flags full.covmat
and
extended.output
:
If full.covmat
is FALSE
then the result is an object of a "similar"
class as newdata
(data frame,
SpatialPointsDataFrame
,
SpatialPixelsDataFrame
SpatialGridDataFrame
,
SpatialPolygonsDataFrame
).
The data frame or the
data
slot of the Spatial...DataFrame
objects
have the following components:
the coordinates of the prediction points (only present if
newdata
is a data frame).
pred
: the Kriging predictions (or fitted values).
se
: the root mean squared prediction errors (Kriging
standard errors).
lower
, upper
: the limits of tolerance/confidence
intervals,
trend
, var.pred
, cov.pred.target
,
var.target
: only present if extended.output
is TRUE
,
see Details.
If full.covmat
is TRUE
then predict.georob
returns a list
with the following components:
pred
: a data frame or a Spatial...DataFrame
object
as described above for
full.covmat = FALSE
.
mse.pred
: the full covariance matrix of the prediction errors,
Y(s)hatY(s) or
s(s)hatS(s)
see Details.
var.pred
: the full covariance matrix of the
Kriging predictions, see Details.
cov.pred.target
: the full covariance matrix of the
predictions and the prediction targets, see Details.
var.target
: the full covariance matrix of the
prediction targets, see Details.
Andreas Papritz [email protected]
Nussbaum, M., Papritz, A., Baltensweiler, A. and Walthert, L. (2012) Organic Carbon Stocks of Swiss Forest Soils, Institute of Terrestrial Ecosystems, ETH Zurich and Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), pp. 51. http://dx.doi.org/10.3929/ethza007555133
K¬ünsch, H. R., Papritz, A., Schwierz, C. and Stahel, W. A. (2011) Robust estimation of the external drift and the variogram of spatial data. Proceedings of the ISI 58th World Statistics Congress of the International Statistical Institute. http://ecollection.library.ethz.ch/eserv/eth:7080/eth708001.pdf
georobIntro
for a description of the model and a brief summary of the algorithms;
georob
for (robust) fitting of spatial linear models;
georobObject
for a description of the class georob
;
profilelogLik
for computing profiles of Gaussian likelihoods;
plot.georob
for display of RE(ML) variogram estimates;
control.georob
for controlling the behaviour of georob
;
georobModelBuilding
for stepwise building models of class georob
;
cv.georob
for assessing the goodness of a fit by georob
;
georobMethods
for further methods for the class georob
;
lgnpp
for unbiased backtransformation of Kriging prediction
of logtransformed data;
georobSimulation
for simulating realizations of a Gaussian process
from model fitted by georob
; and finally
sample.variogram
and fit.variogram.model
for robust estimation and modelling of sample variograms.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38  ## Not run:
data(meuse)
data(meuse.grid)
coordinates(meuse.grid) < ~x+y
meuse.grid.pixdf < meuse.grid
gridded(meuse.grid.pixdf) < TRUE
library(constrainedKriging)
data(meuse.blocks)
r.logzn.rob < georob(log(zinc) ~ sqrt(dist), data = meuse, locations = ~ x + y,
variogram.model = "RMexp", param = c(variance = 0.15, nugget = 0.05, scale = 200),
tuning.psi = 1., control = control.georob(cov.bhat = TRUE, full.cov.bhat = TRUE))
## point predictions of log(Zn)
r.pred.points < predict(r.logzn.rob, newdata = meuse.grid.pixdf,
control = control.predict.georob(extended.output = TRUE, full.covmat = TRUE))
str(r.pred.points$pred@data)
## backtransformation of point predictions
r.backtf.pred.points < lgnpp(r.pred.points)
str(r.pred.points$pred@data)
spplot(r.backtf.pred.points[["pred"]], zcol = "lgn.pred", main = "Zn content")
## predicting mean Zn content for whole area
r.block < lgnpp(r.pred.points, is.block = TRUE, all.pred = r.backtf.pred.points[["pred"]])
r.block
## block predictions of log(Zn)
r.pred.block < predict(r.logzn.rob, newdata = meuse.blocks,
control = control.predict.georob(extended.output = TRUE,
pwidth = 75, pheight = 75))
r.backtf.pred.block < lgnpp(r.pred.block, newdata = meuse.grid)
spplot(r.backtf.pred.block, zcol = "lgn.pred", main = "block means Zn content")
## End(Not run)

Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.