Title:	Empirical Distribution Ordering Inference Framework (EDOIF)
Version:	0.1.4
Maintainer:	Chainarong Amornbunchornvej <grandca@gmail.com>
Description:	A non-parametric framework based on estimation statistics principle. Its main purpose is to infer orders of empirical distributions from different categories based on a probability of finding a value in one distribution that is greater than an expectation of another distribution. Given a set of ordered-pair of real-category values the framework is capable of 1) inferring orders of domination of categories and representing orders in the form of a graph; 2) estimating magnitude of difference between a pair of categories in forms of mean-difference confidence intervals; and 3) visualizing domination orders and magnitudes of difference of categories. The publication of this package is at Chainarong Amornbunchornvej, Navaporn Surasvadi, Anon Plangprasopchok, and Suttipong Thajchayapong (2020) <doi:10.1016/j.heliyon.2020.e05435>.
License:	BSD_3_clause + file LICENSE
URL:	https://github.com/DarkEyes/EDOIF
BugReports:	https://github.com/DarkEyes/EDOIF/issues
Language:	en-US
Encoding:	UTF-8
Depends:	R (≥ 3.5.0), boot
Imports:	distr, igraph, ellipsis, simpleboot, ggplot2 (≥ 3.0)
Suggests:	knitr, rmarkdown, markdown
VignetteBuilder:	knitr
RoxygenNote:	7.3.2
NeedsCompilation:	no
Packaged:	2025-04-28 04:35:05 UTC; zero
Author:	Chainarong Amornbunchornvej [aut, cre]
Repository:	CRAN
Date/Publication:	2025-04-28 07:20:02 UTC

Empirical Distribution Ordering Inference Framework (EDOIF)

Description

EDOIF is a non-parametric framework based on Estimation Statistics principle. Its main purpose is to infer orders of empirical distributions from different categories base on a probability of finding a value in one distribution that greater than the expectation of another distribution.

Given a set of ordered-pair of real-category values the framework is capable of 1) inferring orders of domination of categories and representing orders in the form of a graph; 2) estimating magnitude of difference between a pair of categories in forms of confidence intervals; and 3) visualizing domination orders and magnitudes of difference of categories.

Usage

EDOIF(Values, Group, bootT, alpha, methodType)

Arguments

Value

This class constructor returns an object of EDOIF class.

obj an object of EDOIF class that contains the results of ordering inference that can be print in text mode (print(obj)) or graphic mode (plot(obj)).

The obj consists of the following variables

Author(s)

Chainarong Amornbunchornvej, chai@ieee.org

See Also

Run vignette("EDOIF_demo", package = "EDOIF") in a terminal to learn more details about how to use our package.

Examples

# Generate simulation data
nInv<-100
initMean=10
stepMean=20
std=8
simData1<-c()
simData1$Values<-rnorm(nInv,mean=initMean,sd=std)
simData1$Group<-rep(c("C1"),times=nInv)
simData1$Values<-c(simData1$Values,rnorm(nInv,mean=initMean,sd=std) )
simData1$Group<-c(simData1$Group,rep(c("C2"),times=nInv))
simData1$Values<-c(simData1$Values,rnorm(nInv,mean=initMean+2*stepMean,sd=std) )
simData1$Group<-c(simData1$Group,rep(c("C3"),times=nInv) )
simData1$Values<-c(simData1$Values,rnorm(nInv,mean=initMean+3*stepMean,sd=std) )
simData1$Group<-c(simData1$Group, rep(c("C4"),times=nInv) )
simData1$Values<-c(simData1$Values,rnorm(nInv,mean=initMean+4*stepMean,sd=std) )
simData1$Group<-c(simData1$Group, rep(c("C5"),times=nInv) )

# Performing ordering infernce from simData1

resultObj<-EDOIF(simData1$Values,simData1$Group)

# Print results in text mode

print(resultObj)

# Plot results in graphic mode

plot(resultObj)

SimMixDist function

Description

SimMixDist is a support function for generating samples from mixture distribution. The main purpose of this function is to generate samples from non-normal distribution.

Usage

SimMixDist(nInv, mean, std, p1, p2)

Arguments

Value

This function returns a list of samples V generated by a mixture distribution.

Examples

# Generate simulation data with 100 samples with a mixture distribution
# The distribution consist of the following distributions:
# 1) 10% of uniform distribution range [-400,400];
# 2) 50% of normal distribution with mean = 40 and std =8; and
# 3) 40% of Cauchy distribution with location= 45 and scale = 2.

V<-SimMixDist(nInv=100,mean=40,std=8,p1=0.1,p2=0.5)

SimNonNormalDist function

Description

SimNonNormalDist is a support function for generating samples from mixture distribution. There are five categories. Each categories has nInv samples. Categories C1,C2,C3, and C4 are dominated by C5 but none of them dominate each other.

Usage

SimNonNormalDist(nInv, noisePer)

Arguments

Details

The main purpose of this function is to generate samples that contains domination relation among categories.

Value

This function returns a list of samples Values and their category Group generated by a mixture distribution.

Examples

# Generate simulation data with 100 samples per categories with 10% of uniform noise

simData<-SimNonNormalDist(nInv=100,noisePer=0.1)

bootDiffmeanFunc function

Description

bootDiffmeanFunc is a support function for bootstrapping method. Its main task is to infer mean-difference confidence intervals of distributions for all categories except the first category in idx (idx[2],idx[3],...) minus a target category (idx[1]).

Usage

bootDiffmeanFunc(Group, Values, idx, reps, ci, methodType)

Arguments

Value

This function returns a list of mean-difference confidence intervals of categories idx[2],idx[3],... minus category idx[1].

result a list of objects that contains mean-difference confidence intervals of pairs of distributions. It contains mean-difference confidence intervals of categories idx[2],idx[3],... minus category idx[1].

checkSim3Res function

Description

checkSim3Res is a support function for checking whether an adjacency matrix of inferred a dominant-distribution network adjMat is corrected w.r.t. generator SimNonNormalDist().

Usage

checkSim3Res(adjMat, flag = 0)

Arguments

Value

This function returns precision, recall, and F1-score of inferred adjacency matrix.

Examples

# Generate simulation data with 100 samples per categories

simData<-SimNonNormalDist(nInv=100)

# Performing ordering infernce from simData

resultObj<-EDOIF(simData$Values,simData$Group)

# Compare the inferred adjacency matrix with the ground truth

checkSim3Res(adjMat=resultObj$adjMat)

getADJNetDen function

Description

getADJNetDen is a support function for calculating a network density of a dominant-distribution network.

Usage

getADJNetDen(adjMat)

Arguments

Value

This function returns a value of network density of of a dominant-distribution network for a given adjMat.

Examples

# Generate simulation data with 100 samples per categories

simData<-SimNonNormalDist(nInv=100)

# Performing ordering infernce from simData

resultObj<-EDOIF(simData$Values,simData$Group)

# Get a network density of an adjacency matrix

getADJNetDen(adjMat=resultObj$adjMat)

getConfInv function

Description

getConfInv is a support function for bootstrapping method. Its main purpose is to compute a mean confidence intervals of all distributions.

Usage

getConfInv(Values, Group, GroupList, bootT, alpha, methodType)

Arguments

Value

This function returns a list of mean confidence intervals.

getDominantRADJ function

Description

getDominantRADJ is a support function for inferring a dominant-distribution network using mean-difference confidence intervals.

Usage

getDominantRADJ(MegDiffList, methodType)

Arguments

Value

This function returns an adjacency matrix of a dominant-distribution network adjMat and the corresponding lower-bound of mean difference CIs adjDiffMat.

getMegDiffConfInv function

Description

getMegDiffConfInv is a support function for bootstrapping method. Its main purpose is to compute a mean-difference confidence intervals between all pair of distributions.

Usage

getMegDiffConfInv(Values, Group, GroupList, bootT, alpha, methodType)

Arguments

Value

This function returns a list of mean-difference confidence intervals.

MegDiffList a list of objects that contains mean-difference confidence intervals of all possible pairs of distributions. It contains MegDiffList[[1]],...,MegDiffList[[length(GroupList)]].

The MegDiffList consists of the following variables

getOrder function

Description

getOrder is a support function for inferring a linear order of categories ascendingly sorted by their means.

Usage

getOrder(Values, Group)

Arguments

Value

This function returns two lists: an order list of categories sortedGroupList and its correspoding list of means sortedmeanList.

Examples

# Generate simulation data

simData<-SimNonNormalDist(nInv=100,noisePer=0.1)

# Call the function to get the sorted lists
getOrder(Values=simData$Values,Group=simData$Group)

getWilcoxDominantRADJ function

Description

getWilcoxDominantRADJ is a support function for inferring a dominant-distribution network using Mann-Whitney (Wilcoxon) Test.

Usage

getWilcoxDominantRADJ(Values, Group, GroupList, alpha)

Arguments

Value

This function returns an adjacency matrix of a dominant-distribution network adjMat. and the corresponding p-values of all category pairs.

getiGraphNetDen function

Description

getiGraphNetDen is a support function for calculating a network density of a dominant-distribution network.

Usage

getiGraphNetDen(g)

Arguments

Value

This function returns a value of network density of of a dominant-distribution network for a given object g.

Examples

# Generate simulation data with 100 samples per categories

simData<-SimNonNormalDist(nInv=100)

# Performing ordering infernce from simData

resultObj<-EDOIF(simData$Values,simData$Group)

# Get a network density of an iGraph object

getiGraphNetDen(g=resultObj$gObj)

getiGraphOBJ function

Description

getiGraphOBJ is a support function for converting a dominant-distribution network adjacency matrix to an iGraph object.

Usage

getiGraphOBJ(adjMat, sortedGroupList)

Arguments

Value

This function returns an iGraph object of a dominant-distribution network for a given adjMat.

Examples

# Generate simulation data with 100 samples per categories

simData<-SimNonNormalDist(nInv=100)

# Performing ordering infernce from simData

resultObj<-EDOIF(simData$Values,simData$Group)

# Get an iGraph object from an adjacency matrix

igraphObj<-getiGraphOBJ(adjMat=resultObj$adjMat,sortedGroupList=resultObj$sortedGroupList)

getttestDominantRADJ function

Description

getttestDominantRADJ is a support function for inferring a dominant-distribution network using Student's t-test.

Usage

getttestDominantRADJ(Values, Group, GroupList, alpha)

Arguments

Value

This function returns an adjacency matrix of a dominant-distribution network adjMat and the corresponding p-values of all category pairs.

meanBoot function

Description

meanBoot is a support function for bootstrapping method. Its main purpose is to compute a mean of a given samples from data selected by indices.

Usage

meanBoot(data, indices)

Arguments

Value

This function returns a mean of values in data that have values TRUE within indices.

plot.EDOIF function

Description

plot.EDOIF is a support function for printing all plots of EDOIF framework: dominant-distribution network plot, mean CI plot, and mean-difference CI plot.

Usage

## S3 method for class 'EDOIF'
plot(x, ..., NList, options, fontSize)

Arguments

Examples

# Generate simulation data with 100 samples per categories

simData<-SimNonNormalDist(nInv=100)

# Performing ordering infernce from simData

resultObj<-EDOIF(simData$Values,simData$Group)

# Plot results in graphic mode

plot(resultObj)

plotGraph function

Description

plotGraph is a support function for plotting a dominant-distribution network from an adjacency matrix.

Usage

plotGraph(obj, rankFlag = TRUE)

Arguments

Value

This function returns a list of an object of iGraph for a dominant-distribution network and its plot variable.

Examples

# Generate simulation data with 100 samples per categories

simData<-SimNonNormalDist(nInv=100)

# Performing ordering infernce from simData

resultObj<-EDOIF(simData$Values,simData$Group)

# Plot a dominant-distribution network and return a list of an iGraph object

iGraphList<-plotGraph(obj=resultObj)

plotMeanCIs function

Description

plotMeanCIs is a support function for plotting mean confidence intervals.

Usage

plotMeanCIs(obj, fontSize = 15, rankFlag = TRUE)

Arguments

Value

This function returns a list of an object of ggplot class.

Examples

# Generate simulation data with 100 samples per categories

simData<-SimNonNormalDist(nInv=100)

# Performing ordering infernce from simData

resultObj<-EDOIF(simData$Values,simData$Group)

# Get a list of ggplot object of mean confidence intervals

ggplotList<-plotMeanCIs(obj=resultObj)

# Plot mean confidence intervals

plot(ggplotList$pMeanCI)

plotMeanDiffCIs function

Description

plotMeanDiffCIs is a support function for plotting difference-mean confidence intervals.

Usage

plotMeanDiffCIs(obj, NList, fontSize = 15, rankFlag = TRUE)

Arguments

Value

This function returns a list of an object of ggplot class.

Examples

# Generate simulation data with 100 samples per categories

simData<-SimNonNormalDist(nInv=100)

# Performing ordering infernce from simData

resultObj<-EDOIF(simData$Values,simData$Group)

# Get a list of ggplot object of mean-difference confidence intervals

ggplotList<-plotMeanDiffCIs(obj=resultObj)

# Plot mean-difference confidence intervals

plot(ggplotList$pDiffCI)

print.EDOIF function

Description

print.EDOIF is a support function for printing results of ordering inference in text.

Usage

## S3 method for class 'EDOIF'
print(x, ...)

Arguments

Examples

# Generate simulation data with 100 samples per categories

simData<-SimNonNormalDist(nInv=100)

# Performing ordering infernce from simData

resultObj<-EDOIF(simData$Values,simData$Group)

# Print results in text mode

print(resultObj)