| Type: | Package |
| Title: | Calculation and Visualization of the Impact Effect Size Measure |
| 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. |
| Version: | 0.7 |
| Date: | 2024-04-14 |
| URL: | https://cran.r-project.org/package=ImpactEffectsize |
| Encoding: | UTF-8 |
| Author: | Joern Loetsch [aut,cre] (<https://orcid.org/0000-0002-5818-6958>), Alfred Ultsch [aut] (<https://orcid.org/0000-0002-7845-3283>) |
| Maintainer: | Jorn Lotsch <j.loetsch@em.uni-frankfurt.de> |
| LazyData: | true |
| Imports: | caTools, matrixStats, parallelDist, methods, stats, graphics, Rcpp |
| Suggests: | testthat |
| Depends: | R (≥ 3.5.0) |
| License: | GPL-3 |
| RoxygenNote: | 6.1.1 |
| LinkingTo: | Rcpp |
| NeedsCompilation: | yes |
| Packaged: | 2024-04-14 16:03:27 UTC; joern |
| Repository: | CRAN |
| Date/Publication: | 2024-04-14 16:20:02 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$Classes
Examples
data(FlowcytometricData)
str(FlowcytometricData)
Impact effect size measure
Description
Calculates the Impact effect size measure that is based on the group distance and the difference in the shape of the data distribution between two groups.
Usage
Impact(Data, Cls, PlotIt = FALSE, pde = TRUE,
col = c("red","blue"), meanLines = FALSE, medianLines = FALSE, ...)
Arguments
Data |
the data of both groups as a vector. |
Cls |
the class information as a vector of similar length as the data vector. |
PlotIt |
plots the pdf of the two groups as Pareto density estimation. |
col |
colors of the two lines to plot.
Will be ignored if |
pde |
plots a standard pdf insted of a non-parametric Pareto density estimation.
Will be ignored if |
meanLines |
adds perpendicular lines at the means of the two groups.
Will be ignored if |
medianLines |
adds perpendicular lines at the medians of the two groups.
Will be ignored if |
... |
more graphical parameters can be given as plot arguments if PlotIt = TRUE. |
Value
Returns a list of value incuding the main result and intermediate results.
Impact |
the main effect size measure. |
MorphDiff |
the extend of the group-difference in the shapes of the pdf. |
CTDiff |
the extend of the difference in the group medians. |
Author(s)
Jorn Lotsch and Alfred Ultsch
References
Lotsch, J., and Ultsch, A. (2019): ImpactEffectsize – an R Package for calculation and visualisation of the Impact distance and shape based effect size measure.
Examples
## example 1
data("FeatureselectionData")
ImpactSize <- Impact(Data = FeatureselectionData$Var0011,
Cls = FeatureselectionData$Classes, PlotIt = TRUE)
## example 2
data("BcellLymphomaCD79")
data("FeatureselectionData")
data("FlowcytometricData")
data("SameMeansData")
data("StocksFluctuation")
Example artificial data with two groups of same means but different data distribution shapes.
Description
Dataset with 2 classes six variables were both classes have the same means but different shapes of the distribution.
Usage
data("SameMeansData")Details
Size 2000 x 7 , Dimensions 1, stored in SameMeansData$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)