Description Usage Arguments Details Value Note Author(s) References See Also Examples
Optimize a sample configuration for spatial trend identification and estimation using the method proposed by Minasny and McBratney (2006), known as the conditioned Latin hypercube sampling. An utility function U is defined so that the sample reproduces the marginal distribution and correlation matrix of the numeric covariates, and the class proportions of the factor covariates (CLHS). The utility function is obtained aggregating three objective functions: O1, O2, and O3.
1 2 3 4 5 6 7  optimCLHS(points, candi, covars, use.coords = FALSE,
clhs.version = c("paper", "fortran", "update"),
schedule = scheduleSPSANN(), plotit = FALSE, track = FALSE,
boundary, progress = "txt", verbose = FALSE, weights)
objCLHS(points, candi, covars, use.coords = FALSE,
clhs.version = c("paper", "fortran", "update"), weights)

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. 
use.coords 
(Optional) Logical value. Should the spatial x and ycoordinates be used as covariates?
Defaults to 
clhs.version 
(Optional) Character value setting the CLHS version that should be used. Available
options are: 
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. 
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
.
Reproducing the marginal distribution of the numeric covariates depends upon the definition of marginal
sampling strata. Equalarea marginal sampling strata are defined using the sample quantiles estimated
with quantile
using a continuous function (type = 7
), that is, a function that
interpolates between existing covariate values to estimate the sample quantiles. This is the procedure
implemented in the original method of Minasny and McBratney (2006), which creates breakpoints that do not
occur in the population of existing covariate values. Depending on the level of discretization of the
covariate values, that is, how many significant digits they have, this can create repeated breakpoints,
resulting in empty marginal sampling strata. The number of empty marginal sampling strata will ultimately
depend on the frequency distribution of the covariate and on the number of sampling points. The effect of
these features on the spatial modelling outcome still is poorly understood.
The correlation between two numeric covariates is measured using the sample Pearson's r, a descriptive statistic that ranges from 1 to +1. This statistic is also known as the sample linear correlation coefficient. The effect of ignoring the correlation among factor covariates and between factor and numeric covariates on the spatial modelling outcome still is poorly understood.
A method of solving a multiobjective combinatorial optimization problem (MOCOP) is to aggregate the objective functions into a single utility function U. In the spsann package, as in the original implementation of the CLHS by Minasny and McBratney (2006), the aggregation is performed using the weighted sum method, which uses weights to incorporate the a priori preferences of the user about the relative importance of each objective function. When the user has no preference, the objective functions receive equal weights.
The weighted sum method is affected by the relative magnitude of the different objective function values.
The objective functions implemented in optimCLHS
have different units and orders of magnitude. The
consequence is that the objective function with the largest values, generally O1, may have a numerical
dominance during the optimization. In other words, the weights may not express the true preferences of the
user, resulting that the meaning of the utility function becomes unclear because the optimization will
likely favour the objective function which is numerically dominant.
An efficient solution to avoid numerical dominance is to scale the objective functions so that they are
constrained to the same approximate range of values, at least in the end of the optimization. In the
original implementation of the CLHS by Minasny and McBratney (2006), clhs.version = "paper"
, optimCLHS
uses the naive aggregation method, which ignores that the three objective functions have different units
and orders of magnitude. In a 2015 Fortran implementation of the CLHS, clhs.version = "fortran"
, scaling
factors were included to make the values of the three objective function more comparable. The effect of
ignoring the need to scale the objective functions, or using arbitrary scaling factors, on the spatial
modelling outcome still is poorly understood. Thus, an updated version of O1, O2, and O3 has
been implemented in the spsann package. The need formulation aim at making the values returned by the
objective functions more comparable among themselves without having to resort to arbitrary scaling factors.
The effect of using these new formulations have not been tested yet.
optimCLHS
returns an object of class OptimizedSampleConfiguration
: the optimized sample configuration
with details about the optimization.
objCLHS
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.
The (only?) difference of optimCLHS
to the original Fortran implementation of Minasny and McBratney
(2006), and to the clhs
function implemented in the former
clhs package by Pierre Roudier, is
the annealing schedule.
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  data(meuse.grid, package = "sp")
candi < meuse.grid[1:1000, 1:2]
covars < meuse.grid[1:1000, 5]
schedule < scheduleSPSANN(
chains = 1, initial.temperature = 20, x.max = 1540, y.max = 2060,
x.min = 0, y.min = 0, cellsize = 40)
set.seed(2001)
res < optimCLHS(
points = 10, candi = candi, covars = covars, use.coords = TRUE,
clhs.version = "fortran", weights = list(O1 = 0.5, O3 = 0.5), schedule = schedule)
objSPSANN(res)  objCLHS(
points = res, candi = candi, covars = covars, use.coords = TRUE,
clhs.version = "fortran", weights = list(O1 = 0.5, O3 = 0.5))

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