opdisDownsampling: Optimal Distribution Preserving Down-Sampling of Bio-Medical...

In opdisDownsampling: Optimal Distribution Preserving Down-Sampling of Bio-Medical Data

Optimal Distribution Preserving Down-Sampling of Bio-Medical Data

Description

The package provides the necessary functions for optimal distribution-preserving down-sampling of large (bio-medical) data sets.

Usage

opdisDownsampling(Data, Cls, Size, Seed, nTrials = 1000,
TestStat = "ad", MaxCores = getOption("mc.cores", 2L),
  JobSize = 10000, PCAimportance = FALSE)

Arguments

Data	the (numerical!) data as a vector, matrix or data frame.
Cls	the class information, if any, as a vector of similar length as instances in the data.
Size	the total number of instances across all classes to be drawn.
Seed	a predefined seed to modify the results.
nTrials	how many samples to choose from should be randomly drawn.
TestStat	statistical criterion for similarity judgment.
MaxCores	maximum number of cpu cores to use for parallel computing.
JobSize	how many samples can be drawn at once.
PCAimportance	PCA based feature selection; only variables important in PCA projection are considered.

Value

Returns a list of data containing the drawn samples and the omitted data.

ReducedData	the selected sample data and class information.
ReducedData	the not-selected sample data and class information.
ReducedInstances	the instance numbers of the selected sample data.

Author(s)

Jorn Lotsch

References

Lotsch, J., Malkusch, S., Ultsch, A. (2021): Optimal distribution-preserving downsampling of large biomedical data sets (opdisDownsampling). PLoS One. 2021 Aug 5;16(8):e0255838. doi: 10.1371/journal.pone.0255838. eCollection 2021.

Examples

## example 1
data(iris)
Iris50percent <- opdisDownsampling(Data = iris[,1:4], Cls = as.integer(iris$Species),
  Size = 50, MaxCores = 1)

opdisDownsampling documentation built on May 24, 2022, 5:05 p.m.