partition (version 0.1.0)

part_kmeans: Partitioner: K-means, ICC, scaled means

Description

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().

part_kmeans() uses the following direct-measure-reduce approach:

• direct: direct_k_cluster(), K-Means Clusters

• measure: measure_min_icc(), Minimum Intraclass Correlation

• reduce: reduce_kmeans(), Scaled Row Means

Usage

part_kmeans(algorithm = c("armadillo", "Hartigan-Wong", "Lloyd", "Forgy",
  "MacQueen"), search = c("binary", "linear"), init_k = NULL,
  n_hits = 4)

Arguments

algorithm

The K-Means algorithm to use. The default is a fast version of the LLoyd algorithm written in armadillo. The rest are options in kmeans(). In general, armadillo is fastest, but the other algorithms can be faster in high dimensions.

search

The search method. Binary search is generally more efficient but linear search can be faster in very low dimensions.

init_k

The initial k to test. If NULL, then the initial k is the threshold times the number of variables.

n_hits

In linear search method, the number of iterations that should be under the threshold before reducing; useful for preventing false positives.

Value

a partitioner

See Also

Other partitioners: as_partitioner, part_icc, part_minr2, part_pc1, part_stdmi, replace_partitioner

Examples

# NOT RUN {
set.seed(123)
df <- simulate_block_data(c(3, 4, 5), lower_corr = .4, upper_corr = .6, n = 100)

# fit partition using part_kmeans()
partition(df, threshold = .6, partitioner = part_kmeans())

# }