Optimize a sample configuration for spatial interpolation with a known linear model. A criterion is defined so that the sample configuration minimizes the mean or maximum kriging variance (MKV).
optimMKV(points, candi, covars, eqn = z ~ 1, vgm, krige.stat = "mean",
..., schedule = scheduleSPSANN(), plotit = FALSE, track = FALSE,
boundary, progress = "txt", verbose = FALSE)objMKV(points, candi, covars, eqn = z ~ 1, vgm, krige.stat = "mean",
...)
Integer value, integer vector, data frame or matrix, or list.
Integer value. The number of points. These points will be randomly sampled from candi to form
the starting sample configuration.
Integer vector. The row indexes of candi that correspond to the points that form the starting
sample configuration. The length of the vector defines the number of points.
Data frame or matrix. An object with three columns in the following order: [, "id"], the
row indexes of candi that correspond to each point, [, "x"], the projected x-coordinates, and
[, "y"], the projected y-coordinates.
List. An object with two named sub-arguments: fixed, a data frame or matrix with the projected
x- and y-coordinates of the existing sample configuration -- kept fixed during the optimization --, and
free, an integer value defining the number of points that should be added to the existing sample
configuration -- free to move during the optimization.
Data frame or matrix with the candidate locations for the jittered points. candi must
have two columns in the following order: [, "x"], the projected x-coordinates, and [, "y"],
the projected y-coordinates.
Data frame or matrix with the covariates in the columns.
Formula string that defines the dependent variable z as a linear model of the independent
variables contained in covars. Defaults to eqn = z ~ 1, that is, ordinary kriging. See the
argument formula in the function krige for more information.
Object of class variogramModel. See the argument model in the function
krige for more information.
Character value defining the statistic that should be used to summarize the kriging
variance. Available options are "mean" and "max" for the mean and maximum kriging variance,
respectively. Defaults to krige.stat = "mean".
further arguments passed to krige.
List with 11 named sub-arguments defining the control parameters of the cooling schedule.
See scheduleSPSANN.
(Optional) Logical for plotting the optimization results, including a) the progress of the
objective function, and b) the starting (gray circles) and current sample configuration (black dots), and
the maximum jitter in the x- and y-coordinates. The plots are updated at each 10 jitters. When adding
points to an existing sample configuration, fixed points are indicated using black crosses. Defaults to
plotit = FALSE.
(Optional) Logical value. Should the evolution of the energy state be recorded and returned
along with the result? If track = FALSE (the default), only the starting and ending energy states
are returned along with the results.
(Optional) SpatialPolygon defining the boundary of the spatial domain. If missing and
plotit = TRUE, boundary is estimated from candi.
(Optional) Type of progress bar that should be used, with options "txt", for a text
progress bar in the R console, "tk", to put up a Tk progress bar widget, and NULL to omit the
progress bar. A Tk progress bar widget is useful when using parallel processors. Defaults to
progress = "txt".
(Optional) Logical for printing messages about the progress of the optimization. Defaults to
verbose = FALSE.
optimMKV returns an object of class OptimizedSampleConfiguration: the optimized sample
configuration with details about the optimization.
objMKV returns a numeric value: the energy state of the sample configuration -- the objective
function value.
Details about the mechanism used to generate a new sample configuration out of the current sample
configuration by randomly perturbing the coordinates of a sample point are available in the help page of
spJitter.
Brus, D. J.; Heuvelink, G. B. M. Optimization of sample patterns for universal kriging of environmental variables. Geoderma. v. 138, p. 86-95, 2007.
Heuvelink, G. B. M.; Brus, D. J.; de Gruijter, J. J. Optimization of sample configurations for digital mapping of soil properties with universal kriging. In: Lagacherie, P.; McBratney, A. & Voltz, M. (Eds.) Digital soil mapping - an introductory perspective. Elsevier, v. 31, p. 137-151, 2006.
# NOT RUN {
data(meuse.grid, package = "sp")
candi <- meuse.grid[1:1000, 1:2]
covars <- as.data.frame(meuse.grid)[1:1000, ]
vgm <- gstat::vgm(psill = 10, model = "Exp", range = 500, nugget = 8)
schedule <- scheduleSPSANN(
initial.temperature = 10, chains = 1, x.max = 1540, y.max = 2060,
x.min = 0, y.min = 0, cellsize = 40)
set.seed(2001)
res <- optimMKV(
points = 10, candi = candi, covars = covars, eqn = z ~ dist,
vgm = vgm, schedule = schedule)
objSPSANN(res) - objMKV(
points = res, candi = candi, covars = covars, eqn = z ~ dist,
vgm = vgm)
# }
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