| Type: | Package |
| Title: | Calculation and Visualization of the Impact Effect Size Measure |
| Version: | 0.8 |
| Date: | 2025-09-02 |
| Description: | A non-parametric effect size measure capturing changes in central tendency or shape of data distributions. The package provides the necessary functions to calculate and plot the Impact effect size measure between two groups. |
| URL: | https://cran.r-project.org/package=ImpactEffectsize |
| Encoding: | UTF-8 |
| LazyData: | true |
| Imports: | caTools, matrixStats, parallelDist, methods, stats, graphics, Rcpp, withr |
| Suggests: | testthat |
| Depends: | R (≥ 3.5.0) |
| License: | GPL-3 |
| RoxygenNote: | 6.1.1 |
| LinkingTo: | Rcpp |
| NeedsCompilation: | yes |
| Packaged: | 2025-09-02 17:31:23 UTC; joern |
| Author: | Jorn Lotsch  [aut,
cre],
Alfred Ultsch
[aut] |
| Maintainer: | Jorn Lotsch <j.loetsch@em.uni-frankfurt.de> |
| Repository: | CRAN |
| Date/Publication: | 2025-09-02 18:00:02 UTC |
ImpactEffectsize-package
Description
Calculation and visualization of the Impact effect size measure. A non-parametric effect size measure capturing changes in central tendency or shape of data distributions for feature selection preceding machine-learning. The package provides the necessary functions to calculate and plot the Impact effect size measure between two groups.
References
Lotsch, J., and Ultsch, A. (2019): Impact – An R Package for calculation and visualisation of the Impact distance and data distribution-shape based effect size measure.
Example data of bimodal CD79 expression.
Description
Data set of CD79 expression in patients with B lymphoma (class 1) and in controls (class 2).
Usage
data("BcellLymphomaCD79")Details
Size 258429 x 2 , Dimensions 1, stored in BcellLymphomaCD79$Data
Classes 2, stored in BcellLymphomaCD79$Classes
Examples
data(BcellLymphomaCD79)
str(BcellLymphomaCD79)
Example data with two groups and the Impact effet size measure.
Description
Dataset with 2 classes and 20 variables that allow class separation at varous degrees of difficulty.
Usage
data("FeatureselectionData")Details
Size 2000 x 20 , Dimensions 1, stored in FeatureselectionData$Var0001,...,FeatureselectionData$Var0020
Classes 2, stored in FeatureselectionData$Classes
Examples
data(FeatureselectionData)
str(FeatureselectionData)
Example data of hematologic marker expression.
Description
Data set of 8 flow cytometry-based lymphoma makers from 1,494 cells from healthy subjects (class 1) and 1,302 cells from lymphoma patients (class 2).
Usage
data("FlowcytometricData")Details
Size 2796 x 9 , Dimensions 1, stored in FlowcytometricData$$[CD3,CD4,CD8,CD11,CD19,CD103,CD200,IgM]
Classes 2, stored in FlowcytometricData$Classes
Examples
data(FlowcytometricData)
str(FlowcytometricData)
Impact effect size measure
Description
Calculates the Impact effect size measure that is based on the group distance and the difference in the shape of the data distribution between two groups.
Usage
Impact(Data, Cls, PlotIt = FALSE, pde = TRUE,
col = c("red","blue"), meanLines = FALSE, medianLines = FALSE, ...)
Arguments
Data |
Numeric vector containing the values of both groups. |
Cls |
Grouping vector or factor of same length as Data, containing exactly two distinct classes. |
PlotIt |
Logical; if TRUE, plots the probability density function (PDF) of the two groups using Pareto density estimation or kernel density as fallback. |
pde |
Logical; if TRUE, attempts Pareto Density Estimation (PDE) for the PDFs in the plot. Ignored if |
col |
Character vector of length two specifying colors for the two groups in the plot. Ignored if |
meanLines |
Logical; if TRUE, draws vertical lines at group means in the plot. Ignored if |
medianLines |
Logical; if TRUE, draws vertical lines at group medians in the plot. Ignored if |
... |
Further graphical parameters passed to the plotting function if |
Details
The Impact effect size measure combines central tendency differences (based on group medians) and morphological differences (based on Pareto density estimation). If Pareto density estimation fails or is disabled, the density plots fallback to standard kernel density estimates. The function can optionally plot these densities along with mean and/or median reference lines.
Value
Returns a list with the following components:
- Impact
Numeric scalar; the combined effect size measure based on difference in medians and distribution shapes.
- MorphDiff
Numeric scalar; the extent of difference in shapes of the probability density functions.
- CTDiff
Numeric scalar; the extent of difference in group medians.
- density_df
Data frame with columns
PDEKernels,pde_Cls1,pde_Cls2containing the density kernel points and Pareto densities for each group. This may be empty if Pareto density estimation was not successful or disabled.
Author(s)
Jorn Lotsch and Alfred Ultsch
References
Lotsch, J., and Ultsch, A. (2019): ImpactEffectsize – an R Package for calculation and visualisation of the Impact distance and shape based effect size measure.
Examples
## Example 1: Use Impact with plotting
data("FeatureselectionData")
ImpactSize <- Impact(Data = FeatureselectionData$Var0011,
Cls = FeatureselectionData$Classes, PlotIt = TRUE)
## Example 2: Impact without plotting
ImpactSize <- Impact(Data = FeatureselectionData$Var0011,
Cls = FeatureselectionData$Classes, PlotIt = FALSE)
## example 2
data("BcellLymphomaCD79")
data("FeatureselectionData")
data("FlowcytometricData")
data("SameMeansData")
data("StocksFluctuation")
Example artificial data with two groups of same means but different data distribution shapes.
Description
Dataset with 2 classes six variables were both classes have the same means but different shapes of the distribution.
Usage
data("SameMeansData")Details
Size 2000 x 7 , Dimensions 1, stored in SameMeansData$NOchangeInMandS,...,SameMeansData$NegChi2andGauss
Classes 2, stored in SameMeansData$Classes
Examples
data(SameMeansData)
str(SameMeansData)
Example data of stock fluctuation.
Description
Data set of Log ratios of daily changes of n =5,522 for 10 German stocks with low fluctuation (class 1) or high fluctuation (class 2).
Usage
data("StocksFluctuation")Details
Size 5522 x 2 , Dimensions 1, stored in StocksFluctuation$logFluctuation
Classes 2, stored in StocksFluctuation$Classes
Examples
data(StocksFluctuation)
str(StocksFluctuation)