This is a wrapper function for first making a map with table data then,
running optimizations to make the map otherwise done with acmap()
followed by optimizeMap()
.
make.acmap(
titer_table = NULL,
ag_names = NULL,
sr_names = NULL,
number_of_dimensions = 2,
number_of_optimizations = 100,
minimum_column_basis = "none",
fixed_column_bases = NULL,
sort_optimizations = TRUE,
check_convergence = TRUE,
verbose = TRUE,
options = list(),
...
)
A table of titer data
A vector of antigen names
A vector of sera names
The number of dimensions in the map
The number of optimization runs to perform
The minimum column basis for the map
A vector of fixed values to use as column bases directly, rather than calculating them from the titer table.
Should optimizations be sorted by stress afterwards?
Should a basic check for convergence of lowest stress optimization runs onto a similar solution be performed.
Should progress messages be reported, see also
RacOptimizer.options()
List of named optimizer options, see RacOptimizer.options()
Further arguments to pass to acmap()
Returns an acmap object that has optimization run results.
Other map optimization functions:
RacOptimizer.options()
,
moveTrappedPoints()
,
optimizeMap()
,
randomizeCoords()
,
relaxMapOneStep()
,
relaxMap()