Description Usage Arguments Details Value Note Author(s) References See Also Examples
Optimize a sample configuration for spatial trend identification and estimation. A criterion is defined so that the sample reproduces the bivariate association/correlation between the covariates (CORR).
1 2 3 4 5 6 
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

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
.
The correlation between two numeric covariates is measured using the Pearson's r, a descriptive statistic that ranges from 1 to +1. This statistic is also known as the linear correlation coefficient.
When the set of covariates includes factor covariates, all numeric covariates are transformed into factor covariates. The factor levels are defined using the marginal sampling strata created from one of the two methods available (equalarea or equalrange strata).
The association between two factor covariates is measured using the Cramér's V, a descriptive statistic that ranges from 0 to +1. The closer to +1 the Cramér's V is, the stronger the association between two factor covariates.
The main weakness of using the Cramér's V is that, while the Pearson's r shows the degree and direction of the association between two covariates (negative or positive), the Cramér's V only measures the degree of association (weak or strong). The effect of replacing the Pearson's r with the Cramér's V on the spatial modelling outcome still is poorly understood.
optimCORR
returns an object of class OptimizedSampleConfiguration
: the optimized sample
configuration with details about the optimization.
objCORR
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.
Alessandro SamuelRosa alessandrosamuelrosa@gmail.com
Cramér, H. Mathematical methods of statistics. Princeton: Princeton University Press, p. 575, 1946.
Everitt, B. S. The Cambridge dictionary of statistics. Cambridge: Cambridge University Press, p. 432, 2006.
1 2 3 4 5 6 7 8 9 10 11 12  data(meuse.grid, package = "sp")
candi < meuse.grid[1:1000, 1:2]
covars < meuse.grid[1:1000, 5]
schedule < scheduleSPSANN(
initial.temperature = 5, chains = 1, x.max = 1540, y.max = 2060,
x.min = 0, y.min = 0, cellsize = 40)
set.seed(2001)
res < optimCORR(
points = 10, candi = candi, covars = covars, use.coords = TRUE,
schedule = schedule)
objSPSANN(res)  objCORR(
points = res, candi = candi, covars = covars, use.coords = TRUE)

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