| Title: | Euclidean Distance-Optimized Data Transformation |
|---|---|
| Description: | A data transformation method which takes into account the special property of scale non-invariance with a breakpoint at 1 of the Euclidean distance. |
| Authors: | Jorn Lotsch[aut,cre], Alfred Ultsch[aut] |
| Maintainer: | Jorn Lotsch <[email protected]> |
| License: | GPL-3 |
| Version: | 0.2.5 |
| Built: | 2024-12-11 07:20:23 UTC |
| Source: | CRAN |
The package provides the necessary functions for performing the EDO data transformation.
EDOtrans(Data, Cls, PlotIt = FALSE, FitAlg = "normalmixEM", Criterion = "LR", MaxModes = 8, MaxCores = getOption("mc.cores", 2L), Seed)EDOtrans(Data, Cls, PlotIt = FALSE, FitAlg = "normalmixEM", Criterion = "LR", MaxModes = 8, MaxCores = getOption("mc.cores", 2L), Seed)
Data |
the data as a vector. |
Cls |
the class information, if any, as a vector of similar length as instances in the data. |
PlotIt |
whether to plot the fit directly. |
FitAlg |
which fit algorithm to use: "ClusterRGMM" = GMM from ClusterR, "densityMclust" from mclust, "DO" from DistributionOptimization (slow), "MCMC" = NMixMCMC from mixAK, or "normalmixEM" from mixtools. |
Criterion |
which criterion should be used to establish the number of modes from the best GMM fit: "AIC", "BIC", "FM", "GAP", "LR" (likelihood ratio test), "NbClust" (from NbClust), "SI" (Silverman). |
MaxModes |
for automated GMM assessment: the maximum number of modes to be tried. |
MaxCores |
for automated GMM assessment: the maximum number of processor cores used under Unix. |
Seed |
seed parameter set internally. |
Returns a list of transformed data and class assignments.
DataEDO |
the EDO transformed data. |
EDOfactor |
the factor by which each data value has been divided. |
Cls |
the class information for each data instance. |
Jorn Lotsch and Alfred Ultsch
Lotsch, J., Ultsch, A. (2021): EDOtrans – an R Package for Euclidean distance-optimized data transformation.
## example 1 data(iris) IrisEDOdata <- EDOtrans(Data = as.vector(iris[,1]), Cls = as.integer(iris$Species))## example 1 data(iris) IrisEDOdata <- EDOtrans(Data = as.vector(iris[,1]), Cls = as.integer(iris$Species))
Data set of 4 flow cytometry-based lymphoma makers from 1559 cells from healthy subjects (class 1) and 1441 cells from lymphoma patients (class 2).
data("FACSdata")data("FACSdata")
Size 3000 x 4 , stored in FACSdata$[FS,CDa,CDb,CDd]
Original classes 2, stored in FACSdata$Cls
data(FACSdata) str(FACSdata)data(FACSdata) str(FACSdata)
Dataset of 3000 instances with 3 variables that are Gaussian mixtures and belong to classes Cls = 1, 2, or 3, with different means and standard deviations and equal weights of 0.7, 0.3, and 0.1, respectively.
data("GMMartificialData")data("GMMartificialData")
Size 3000 x 3, stored in GMMartificialData$[Var1,Var2,Var3]
Classes 3, stored in GMMartificialData$Cls
data(GMMartificialData) str(GMMartificialData)data(GMMartificialData) str(GMMartificialData)