measure_variance_explained: Measure the information loss of reduction using the variance...

In USCbiostats/partition: Agglomerative Partitioning Framework for Dimension Reduction

Measure the information loss of reduction using the variance explained.

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_variance_explained() assesses information loss by calculating the variance explained by the first component of a principal components analysis. Because the PCA calculates the components and the variance explained at the same time, if the reducer is reduce_first_component(), then measure_variance_explained() will store the first component for later use to avoid recalculation.

Usage

measure_variance_explained(.partition_step)

Arguments

.partition_step

a partition_step object

Value

a partition_step object

See Also

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

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