Description Usage Arguments Details Value Author(s) Examples
Optimize a sample configuration using a userdefined objective function.
1 2 3 
points 
Integer value, integer vector, data frame or matrix, or list.

candi 
Data frame or matrix with the candidate locations for the jittered points. 
fun 
A function defining the objective function that should be used to evaluate the energy state of the system configuration at each random perturbation of a candidate sample point. See ‘Details’ for more information. 
... 
Other arguments passed to the objective function. See ‘Details’ for more information. 
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

The userdefined objective function fun
must be an object of class function
and
include the argument points
. The argument points
is defined in optimUSER
as a matrix
with three columns: [, 1]
the identification of each sample point given by the respective row
indexes of candi
, [, 2]
the xcoordinates, and [, 3]
the ycoordinates. The
identification is useful to retrieve information from any data matrix used by the objective function
defined by the user.
optimUSER
returns an object of class OptimizedSampleConfiguration
: the optimized sample
configuration with details about the optimization.
Alessandro SamuelRosa alessandrosamuelrosa@gmail.com
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45  ## Not run:
# This example takes more than 5 seconds
require(sp)
require(SpatialTools)
data(meuse.grid)
candi < meuse.grid[, 1:2]
schedule < scheduleSPSANN(chains = 1, initial.temperature = 30,
x.max = 1540, y.max = 2060, x.min = 0,
y.min = 0, cellsize = 40)
# Define the objective function  number of points per lag distance class
objUSER <
function (points, lags, n_lags, n_pts) {
dm < SpatialTools::dist1(points[, 2:3])
ppl < vector()
for (i in 1:n_lags) {
n < which(dm > lags[i] & dm <= lags[i + 1], arr.ind = TRUE)
ppl[i] < length(unique(c(n)))
}
distri < rep(n_pts, n_lags)
res < sum(distri  ppl)
}
lags < seq(1, 1000, length.out = 10)
# Run the optimization using the userdefined objective function
set.seed(2001)
timeUSER < Sys.time()
resUSER < optimUSER(points = 10, fun = objUSER, lags = lags, n_lags = 9,
n_pts = 10, candi = candi, schedule = schedule)
timeUSER < Sys.time()  timeUSER
# Run the optimization using the respective function implemented in spsann
set.seed(2001)
timePPL < Sys.time()
resPPL < optimPPL(points = 10, candi = candi, lags = lags,
schedule = schedule)
timePPL < Sys.time()  timePPL
# Compare results
timeUSER
timePPL
lapply(list(resUSER, resPPL), countPPL, candi = candi, lags = lags)
objSPSANN(resUSER)  objSPSANN(resPPL)
## End(Not run)

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