measure_min_icc: Measure the information loss of reduction using the minimum...

In USCbiostats/partition: Agglomerative Partitioning Framework for Dimension Reduction

Measure the information loss of reduction using the minimum intraclass correlation coefficient

Description

Metrics are functions that tell how much information would be lost for a given reduction in the data. reduce. as_measure() is a helper function to create new metrics to be used in partitioners. partitioners can be created with as_partitioner().

measure_min_icc() assesses information loss by calculating the intraclass correlation coefficient for each set of the target variables and finding their minimum.

Usage

measure_min_icc(.partition_step, search_method = c("binary", "linear"))

Arguments

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

Value

a partition_step object

See Also

Other metrics: as_measure(), measure_icc(), measure_min_r2(), measure_std_mutualinfo(), measure_variance_explained()

USCbiostats/partition documentation built on Nov. 11, 2024, 4:25 p.m.