shuffle_grouped_data function

Generate in one go a shuffling function that produces permutations with specific constraints on multiple sample variables and group sizes fitting one specific allocation variable

shuffle_grouped_data(
  batch_container,
  allocate_var,
  keep_together_vars = c(),
  keep_separate_vars = c(),
  n_min = NA,
  n_max = NA,
  n_ideal = NA,
  subgroup_var_name = NULL,
  report_grouping_as_attribute = FALSE,
  prefer_big_groups = FALSE,
  strict = TRUE,
  fullTree = FALSE,
  maxCalls = 1e+06
)

Arguments

• batch_container: Batch container with all samples assigned that are to be grouped and sub-grouped
• allocate_var: Name of a variable in the samples table to inform possible groupings, as (sub)group sizes must add up to the correct totals
• keep_together_vars: Vector of column names in sample table; groups are formed by pooling samples with identical values of all those variables
• keep_separate_vars: Vector of column names in sample table; items with identical values in those variables will not be put into the same subgroup if at all possible
• n_min: Minimal number of samples in one sub(!)group; by default 1
• n_max: Maximal number of samples in one sub(!)group; by default the size of the biggest group
• n_ideal: Ideal number of samples in one sub(!)group; by default the floor or ceiling of mean(n_min,n_max), depending on the setting of prefer_big_groups
• subgroup_var_name: An optional column name for the subgroups which are formed (or NULL)
• report_grouping_as_attribute: Boolean, if TRUE, add an attribute table to the permutation functions' output, to be used in scoring during the design optimization
• prefer_big_groups: Boolean; indicating whether or not bigger subgroups should be preferred in case of several possibilities
• strict: Boolean; if TRUE, subgroup size constraints have to be met strictly, implying the possibility of finding no solution at all
• fullTree: Boolean: Enforce full search of the possibility tree, independent of the value of maxCalls
• maxCalls: Maximum number of recursive calls in the search tree, to avoid long run times with very large trees

Returns

Shuffling function that on each call returns an index vector for a valid sample permutation