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,
  verbose = TRUE,
  options = list(),
  ...
)

Arguments

titer_table

A table of titer data

ag_names

A vector of antigen names

sr_names

A vector of sera names

number_of_dimensions

The number of dimensions in the map

number_of_optimizations

The number of optimization runs to perform

minimum_column_basis

The minimum column basis for the map

fixed_column_bases

A vector of fixed values to use as column bases directly, rather than calculating them from the titer table.

sort_optimizations

Should optimizations be sorted by stress afterwards?

verbose

Should progress messages be reported, see also RacOptimizer.options()

options

List of named optimizer options, see RacOptimizer.options()

...

Further arguments to pass to acmap()

Value

Returns an acmap object that has optimization run results.

See also