partition (version 0.1.0)

partition: Agglomerative partitioning

Description

partition() reduces data while minimizing information loss using an agglomerative partitioning algorithm. The partition algorithm is fast and flexible: at every iteration, partition() uses an approach called Direct-Measure-Reduce (see Details) to create new variables that maintain the user-specified minimum level of information. Each reduced variable is also interpretable: the original variables map to one and only one variable in the reduced data set.

Usage

partition(.data, threshold, partitioner = part_icc(),
  tolerance = 1e-04, niter = NULL, x = "reduced_var", .sep = "_")

Arguments

.data

a data.frame to partition

threshold

the minimum proportion of information explained by a reduced variable; threshold sets a boundary for information loss because each reduced variable must explain at least as much as threshold as measured by the metric.

partitioner

a partitioner. See the part_*() functions and as_partitioner().

tolerance

a small tolerance within the threshold; if a reduction is within the threshold plus/minus the tolerance, it will reduce.

niter

the number of iterations. By default, it is calculated as 20 the number of variables or 10, whichever is larger.

x

the prefix of the new variable names

.sep

a character vector that separates x from the number (e.g. "reduced_var_1").

Value

a partition object

Details

partition() uses an approach called Direct-Measure-Reduce. Directors tell the partition algorithm what to reduce, metrics tell it whether or not there will be enough information left after the reduction, and reducers tell it how to reduce the data. Together these are called a partitioner. The default partitioner for partition() is part_icc(): it finds pairs of variables to reduce by finding the pair with the minimum distance between them, it measures information loss through ICC, and it reduces data using scaled row means. There are several other partitioners available (part_*() functions), and you can create custom partitioners with as_partitioner() and replace_partitioner().

See Also

part_icc(), part_kmeans(), part_minr2(), part_pc1(), part_stdmi(), as_partitioner(), replace_partitioner()

Examples

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

#  don't accept reductions where information < .6
prt <- partition(df, threshold = .6)
prt

# return reduced data
partition_scores(prt)

# access mapping keys
mapping_key(prt)
unnest_mappings(prt)

# use a lower threshold of information loss
partition(df, threshold = .5, partitioner = part_kmeans())

# use a custom partitioner
part_icc_rowmeans <- replace_partitioner(part_icc, reduce = as_reducer(rowMeans))
partition(df, threshold = .6, partitioner = part_icc_rowmeans)

# }

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