Description Usage Arguments Details Value Note Author(s) References See Also Examples
Optimize a sample configuration for spatial trend identification and estimation. An utility function U is defined so that the sample reproduces the bivariate association/correlation between the covariates, as well as their marginal distribution (ACDC). The utility function is obtained aggregating two objective functions: CORR and DIST.
1 2 3 4 5 6 7 8 9  optimACDC(points, candi, covars, strata.type = "area",
use.coords = FALSE, schedule = scheduleSPSANN(), plotit = FALSE,
track = FALSE, boundary, progress = "txt", verbose = FALSE,
weights, nadir = list(sim = NULL, seeds = NULL, user = NULL, abs =
NULL), utopia = list(user = NULL, abs = NULL))
objACDC(points, candi, covars, strata.type = "area",
use.coords = FALSE, weights, nadir = list(sim = NULL, seeds = NULL,
user = NULL, abs = NULL), utopia = list(user = NULL, abs = NULL))

points 
Integer value, integer vector, data frame or matrix, or list.

candi 
Data frame or matrix with the candidate locations for the jittered points. 
covars 
Data frame or matrix with the covariates in the columns. 
strata.type 
(Optional) Character value setting the type of stratification that should be used to
create the marginal sampling strata (or factor levels) for the numeric covariates. Available options are

use.coords 
(Optional) Logical value. Should the spatial x and ycoordinates be used as covariates?
Defaults to 
schedule 
List with 11 named subarguments defining the control parameters of the cooling schedule.
See 
plotit 
(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 ycoordinates. 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

track 
(Optional) Logical value. Should the evolution of the energy state be recorded and returned
along with the result? If 
boundary 
(Optional) SpatialPolygon defining the boundary of the spatial domain. If missing and

progress 
(Optional) Type of progress bar that should be used, with options 
verbose 
(Optional) Logical for printing messages about the progress of the optimization. Defaults to

weights 
List with named subarguments. The weights assigned to each one of the objective functions that form the multiobjective combinatorial optimization problem. They must be named after the respective objective function to which they apply. The weights must be equal to or larger than 0 and sum to 1. 
nadir 
List with named subarguments. Three options are available: 1) 
utopia 
List with named subarguments. Two options are available: 1) 
The help page of minmaxPareto
contains details on how spsann solves the
multiobjective combinatorial optimization problem of finding a globally optimum sample configuration that
meets multiple, possibly conflicting, sampling objectives.
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
.
Visit the help pages of optimCORR
and optimDIST
to see the
details of the objective functions that compose ACDC.
optimACDC
returns an object of class OptimizedSampleConfiguration
: the optimized sample
configuration with details about the optimization.
objACDC
returns a numeric value: the energy state of the sample configuration – the objective
function value.
The distance between two points is computed as the Euclidean distance between them. This computation assumes that the optimization is operating in the twodimensional Euclidean space, i.e. the coordinates of the sample points and candidate locations should not be provided as latitude/longitude. spsann has no mechanism to check if the coordinates are projected: the user is responsible for making sure that this requirement is attained.
This function was derived with modifications from the method known as the conditioned Latin Hypercube sampling originally proposed by Minasny and McBratney (2006), and implemented in the Rpackage clhs by Pierre Roudier.
Alessandro SamuelRosa alessandrosamuelrosa@gmail.com
Minasny, B.; McBratney, A. B. A conditioned Latin hypercube method for sampling in the presence of ancillary information. Computers & Geosciences, v. 32, p. 13781388, 2006.
Minasny, B.; McBratney, A. B. Conditioned Latin Hypercube Sampling for calibrating soil sensor data to soil properties. Chapter 9. Viscarra Rossel, R. A.; McBratney, A. B.; Minasny, B. (Eds.) Proximal Soil Sensing. Amsterdam: Springer, p. 111119, 2010.
Roudier, P.; Beaudette, D.; Hewitt, A. A conditioned Latin hypercube sampling algorithm incorporating operational constraints. 5th Global Workshop on Digital Soil Mapping. Sydney, p. 227231, 2012.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15  data(meuse.grid, package = "sp")
candi < meuse.grid[1:1000, 1:2]
nadir < list(sim = 10, seeds = 1:10)
utopia < list(user = list(DIST = 0, CORR = 0))
covars < meuse.grid[1:1000, 5]
schedule < scheduleSPSANN(
chains = 1, initial.temperature = 5, x.max = 1540, y.max = 2060,
x.min = 0, y.min = 0, cellsize = 40)
set.seed(2001)
res < optimACDC(
points = 10, candi = candi, covars = covars, nadir = nadir, use.coords = TRUE,
utopia = utopia, schedule = schedule, weights = list(DIST = 1/2, CORR = 1/2))
objSPSANN(res)  objACDC(
points = res, candi = candi, covars = covars, use.coords = TRUE, nadir = nadir,
utopia = utopia, weights = list(DIST = 1/2, CORR = 1/2))

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