Partitioners are functions that tell the partition algorithm 1)
what to try to reduce 2) how to measure how much information is lost from
the reduction and 3) how to reduce the data. In partition, functions that
handle 1) are called directors, functions that handle 2) are called
metrics, and functions that handle 3) are called reducers. partition has a
number of pre-specified partitioners for agglomerative data reduction.
Custom partitioners can be created with as_partitioner().
Pass partitioner objects to the partitioner argument of partition().
as_partitioner(direct, measure, reduce)a function that directs, possibly created by as_director()
a function that measures, possibly created by as_measure()
a function that reduces, possibly created by as_reducer()
a partitioner
Other partitioners:
part_icc(),
part_kmeans(),
part_minr2(),
part_pc1(),
part_stdmi(),
replace_partitioner()
as_partitioner(
direct = direct_distance_pearson,
measure = measure_icc,
reduce = reduce_scaled_mean
)
#> Director: Minimum Distance (Pearson)
#> Metric: Intraclass Correlation
#> Reducer: Scaled Mean