The new version of the spsann package includes some bug fixes and a few modifications. Users now can choose how optimCLHS computes objective function values: as in the original paper or as in the FORTRAN implementation. Users now also must inform the weights passed to optimCLHS as to guarantee that s/he is aware of what s/he is doing. The same apples to other functions that deal with multi-objective optimization problems: optimACDC and optimSPAM. Another important modification in the current version of spsann is the possibility to use a finite set of candidate locations by setting cellsize = 0. This is useful when optimizing sample points only in the feature space and should reduce the computation time needed to find the solution.
plot.OptimizedSampleConfiguration related to the selection of the information to be displayed.weights of optimCLHS, optimACDC and optimSPAM now is mandatory. The user is now required to set the weights as to guarantee that s/he is aware of what s/he is doing.data.frames that store objective function values.cellsize = 0. When this is done, spsann now checks for neighbouring candidate locations already included in the sample as to avoid duplicated sampling points.optimCLHS computes objective function values.optimCLHS computes objective function values.optimCLHS computes objective function values.Now spsann can be used to augment an existing sample configuration, that is, add new sampling points to a spatial sample configuration generated using spsann or any other means. To do so, when using one of the functions from the family of optim...() functions, the user must pass to the function argument points an object of class list containing two named sub-arguments: fixed, a matrix with the coordinates of the existing sample configuration – kept fixed during the optimization –, and free, the number of sample points that should be added to the existing sample configuration – free to move around during the optimization.
This is a major release of package spsann that includes several conceptual changes. Despite our efforts, it was not possible to guarantee the compatibility with previous versions. We have decided not to deprecate functions and function arguments because (1) this would require deprecating a lot of code and (2) you should first read the updated package documentation to understand the conceptual changes that we have made before you start using it. This is a summary of the changes: * A completely new annealing schedule was implemented. The reason for this modification is that the former annealing schedule showed to be inefficient during our tests. The new annealing schedule is the very simple and most-used schedule proposed by Kirkpatrick et al. (1983). We have also replaced the acceptance criterion with the well-known Metropolis criterion. This new implementation showed to be more efficient in our tests than our early implementation. Setting up this new annealing schedule is done using the new function scheduleSPSANN. * A more elegant solution to jitter the sample points was implemented. It consists of using a finite set of candidate locations that are seen by the algorithm as the centre of grid cells. In the first stage, we select a grid cell with replacement. In the second stage, we select a location within that grid cell using simple random sampling. This guarantees that any location in the sampling region is a candidate location for the jittered sample point. * Solving multi-objective combinatorial optimization problems (MOCOP) has become easier with the creation of the new function minmaxPareto(). This function computes the Pareto maximum and minimum values of the objective functions that compose the MOCOP needed to scale the objective functions to the same approximate range of values. * The user can now chose to follow the progress of the optimization using a text progress bar in the R console or a Tk progress bar widget. A Tk progress bar widget is useful when running spsann in parallel processors. * The output of the optimization is now stored in an object of class OptimizedSampleConfiguration. This object contains three slots. The first (points) holds the coordinates of the optimized sample configuration. The second, spsann, stores information about the settings used with the spatial simulated annealing algorithm. The third, objective, holds the settings used with the chosen objective function. Methods were implemented to retrieve information from the new class, as well as producing plots of the optimized sample configuration. * Package documentation was expanded and adapted to cope with the conceptual changes that were made. It also includes a vignette that gives a short description of the package and its structure, as well as presents a few examples on how to use the package. It is strongly recommended to read the new package documentation and the accompanying vignette before you start using the package. * Finally, bugs were fixed, warning messages were improved, and a faster code was implemented whenever possible.
devel branch was merged into master branch.knitr is the engine used to produce the package vignette.OptimizedSampleConfiguration.OptimizedSampleConfiguration is no longer exported.optimPPL was incorrect because it neglected the fact that, in a full distance matrix, two points a and b form two pairs, i.e. ab and ba. The mistake is due to the fact that we use SpatialTools::dist1 to compute the distance matrix instead of stats::dist.optimPPL.autofun to check the number of accepted jitters in the first chain. If the number of accepted jitters is superior to the value passed to schedule$initial.acceptance, the process continues and a message is printed informing the proportion of jitters that have been accepted.optimCLHS() following the original Fortran code of Budiman Minasny."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 running spsann in parallel processors. The tcltk-package is now a suggested package.base, and due to examples that take more than 5 seconds to run.plotOSC()), with options to display the evolution of the energy state and/or the optimized sample configuration.minmaxPareto()) was optimized to be used with both ACDC and SPAN.OptimizedSampleConfiguration) to store the output of optim functions.optimMKV()-function to avoid errors due to the LDLfactor error of the gstat-package had to be reformulated. We are now using try() with a default value which is returned in case of error.scheduleSPSANN().optimMKV()-function to avoid errors due to the LDLfactor error of the gstat-package.minmaxPareto().x.max and y.max are, by default, set to half of the maximum distance in the x- and y-coordinates of candi, respectively. In the same manner, the argument cutoff of optimPPL() is set, by default, to half of the diagonal of the rectangle of sides x.max and y.max.optimCORR() that was causing the following error: Error in if (new_energy <= old_energy) { : missing value where TRUE/FALSE needed. This bug used to affect optimACDC() and optimSPAN().optimCLHS).objSPSANN()) was created to retrieve the energy state of an optimized sample configuration (OSC) at a given point of the optimization.R CMD check.utils::globalVariables to avoid the R CMD check note no visible binding for global variable [variable name]. Source of the solution: http://stackoverflow.com/a/12429344/3365410.plotit, track, verbose, and iteration. The first three were set to FALSE, while the last was set to 100.optimSPAN() and objSPAN() are now full operational..optimPPLcheck() was renamed as .checkPPL(), and .getLagBreaks() was renamed as .lagsPPL(). Note that the first part of the function name indicates what it does, while the second indicates the objective function to which it applies. This standardization is important to ease the construction of multi-objective optimization problems.covars.type.autofun was created to set-up the covariates (covar).boundary of the spatial domain can now be estimated internally. The user should use the rgeos package if a more precise boundary is needed.obj...() and optim...() functions are included in individual files. These R code chunks are used to automatically build internal functions. Currently, R code chunks are used to check the arguments of the family of optim...() functions, prepare points and candi, set plotting options, estimate the boundary, prepare for jittering, plot and jitter, and prepare the output.obj...() functions may not return the same criterion value of the optimized sample configuration returned by the family of optim...() functions if the number of iterations used in the optimization is equal to 100. The problem seems to disappear if a larger number of iterations is used.spJitterFinite() now tries to find an alternative point if the new point already is included in the sample. The number of tries is equal to the total number of points included in the sample. Because the more points we have, the more likely it is that the candidate point already is included in the sample.spJitterFinite() now returns the old point if the new point already is in the sample. This is to avoid an infinite loop at the end of the optimization when the objective function creates a cluster of points.optimACDC(), including new argument definitions;optimACDC(): optimDIST() and optimCORR();