| opdisDownsampling {opdisDownsampling} | R Documentation |
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), 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. |
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)