| krige.conv {geoR} | R Documentation |
This function performs spatial prediction for fixed covariance parameters using global neighbourhood.
Options available implement the following types of kriging: SK (simple kriging), OK (ordinary kriging), KTE (external trend kriging) and UK (universal kriging).
krige.conv(geodata, coords=geodata$coords, data=geodata$data,
locations, borders, krige, output)
krige.control(type.krige = "ok", trend.d = "cte", trend.l = "cte",
obj.model = NULL, beta, cov.model, cov.pars, kappa,
nugget, micro.scale = 0, dist.epsilon = 1e-10,
aniso.pars, lambda)
geodata |
a list containing elements coords and
data as described next. Typically an object of the class
"geodata" - a geoR data-set. If not provided the arguments
coords and data must be provided instead. |
coords |
an n x 2 matrix or data-frame with the 2-D
coordinates of the n data locations.
By default it takes the
component coords of the argument geodata, if provided. |
data |
a vector with n data values. By default it takes the
component data of the argument geodata, if provided. |
locations |
an N x 2 matrix or data-frame with the 2-D
coordinates of the N prediction locations, or a list for which
the first two components are used. Input is internally checked by the
function check.locations. |
borders |
optional. By default reads the element borders
from the geodata object, if present.
Setting to NULL prevents this behavior.
If a two column matrix defining a polygon is
provided the prediction is performed only at locations inside this polygon. |
krige |
a list defining the model components and the type of
kriging. It can take an output to a call to krige.control or
a list with elements as for the arguments in krige.control.
Default values are assumed for arguments or list elements not provided.
See the description of arguments in `krige.control' below. |
output |
a list specifying output options.
It can take an output to a call to output.control or
a list with elements as for the arguments in output.control.
Default values are assumed for arguments not provided.
See documentation for
output.control for further details. |
type.krige |
type of kriging to be performed. Options are
"SK", "OK" corresponding to simple or ordinary
kriging. Kriging with external trend and universal kriging can be
defined setting type.krige = "OK" and specifying the
trend model using the arguments trend.d and
trend.l. |
trend.d |
specifies the trend (covariate) values at the data
locations.
See documentation of trend.spatial for
further details.
Defaults to "cte". |
trend.l |
specifies the trend (covariate) values at prediction
locations. It must be of the same type as for trend.d.
Only used if prediction locations are provided in the argument
locations. |
obj.model |
a list with the model parameters. Typically an
output of likfit or
variofit. |
beta |
numerical value of the mean (vector) parameter.
Only used if type.krige="SK". |
cov.model |
string indicating the name of the model for the
correlation function. Further details can be found in the
documentation of the function
cov.spatial. |
cov.pars |
a 2 elements vector with values of the covariance parameters sigma^2 (partial sill) and phi (range parameter), respectively. |
kappa |
additional smoothness parameter required by the following correlation
functions: "matern", "powered.exponential", "cauchy" and
"gneiting.matern". |
nugget |
the value of the nugget variance parameter tau^2. Defaults to zero. |
micro.scale |
micro-scale variance. If different from zero, the
nugget variance is divided into 2 terms: micro-scale variance
and measurement error. This affect the precision of the predictions.
Often in practice, these two variance components are indistinguishable but the
distinction can be made here if justifiable. See the section
DETAILS in the documentation of output.control. |
dist.epsilon |
a numeric value. Locations which are separated by a distance less than this value are considered co-located. |
aniso.pars |
parameters for geometric anisotropy
correction. If aniso.pars = FALSE no correction is made, otherwise
a two elements vector with values for the anisotropy parameters
must be provided. Anisotropy correction consists of a
transformation of the data and prediction coordinates performed
by the function coords.aniso. |
lambda |
numeric value of the Box-Cox transformation parameter. The value lambda = 1 corresponds to no transformation and lambda = 0 corresponds to the log-transformation. Prediction results are back-transformed and returned is the same scale as for the original data. |
According to the arguments provided, one of the following different types of kriging: SK, OK, UK or KTE is performed. Defaults correspond to ordinary kriging.
An object of the class kriging.
The attribute prediction.locations containing the name of the
object with the coordinates of the prediction locations (argument
locations) is assigned to the object.
Returns a list with the following components:
predict |
a vector with predicted values. |
krige.var |
a vector with predicted variances. |
beta.est |
estimates of the beta, parameter
implicit in kriging procedure. Not included if type.krige = "SK". |
simulations |
an ni x n.sim matrix where ni is the
number of prediction locations. Each column corresponds to a
conditional simulation of the predictive distribution.
Only returned if n.sim > 0. |
message |
messages about the type of prediction performed. |
call |
the function call. |
Other results can be included depending on the options passed to
output.control.
Paulo J. Ribeiro Jr. paulojus@leg.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.
output.control sets output options,
image.kriging and persp.kriging
for graphical output of the results,
krige.bayes for Bayesian prediction and ksline
for a different implementation of kriging allowing for moving
neighborhood. For model fitting see likfit or variofit.
## Not run:
# Defining a prediction grid
loci <- expand.grid(seq(0,1,l=21), seq(0,1,l=21))
# predicting by ordinary kriging
kc <- krige.conv(s100, loc=loci,
krige=krige.control(cov.pars=c(1, .25)))
# mapping point estimates and variances
par.ori <- par(no.readonly = TRUE)
par(mfrow=c(1,2), mar=c(3.5,3.5,1,0), mgp=c(1.5,.5,0))
image(kc, main="kriging estimates")
image(kc, val=sqrt(kc$krige.var), main="kriging std. errors")
# Now setting the output to simulate from the predictive
# (obtaining conditional simulations),
# and to compute quantile and probability estimators
s.out <- output.control(n.predictive = 1000, quant=0.9, thres=2)
set.seed(123)
kc <- krige.conv(s100, loc = loci,
krige = krige.control(cov.pars = c(1,.25)),
output = s.out)
par(mfrow=c(2,2))
image(kc, val=kc$simul[,1], main="a cond. simul.")
image(kc, val=kc$simul[,1], main="another cond. simul.")
image(kc, val=(1 - kc$prob), main="Map of P(Y > 2)")
image(kc, val=kc$quant, main="Map of y s.t. P(Y < y) = 0.9")
par(par.ori)
## End(Not run)