EDOIF: Empirical Distribution Ordering Inference Framework (EDOIF)

In EDOIF: 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

Values	is a vector of real-number values
Group	is a vector of categories of each real number in Values
bootT	is a number of times of sample with replacement for bootstrapping. The default is 1000. It must be above zero
alpha	is a significance level using in both confidence intervals and ordering inference it has the range [0,1]. The default is 0.05.
methodType	is an option for bootstrapping methods:either "perc" or "bca". The "perc" is the default option.

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

Values, Group	The main inputs of the framework. They are the double and character vectors respectively.
bootT, alpha, methodType	The number of bootstrapping, significance level, and bootstrapping method parameters.
sortedGroupList	A list of names of categories ascendingly ordered by their means.
sortedmeanList	A list of means of categories that are ascendingly ordered.
MegDiffList[[i]]	Mean difference confidence intervals and related information of all categories that have higher means than sortedGroupList[i] category.
confInvsList[i,]	A mean confidence interval of sortedGroupList[i] category. confInvsList[i,1] is a lower bound and confInvsList[i,2] is an upper bound.
adjMat[i,j]	An element of adjacency matrix: one if sortedGroupList[j] category dominates sortedGroupList[i] using Mann-Whitney test, otherwise zero.
pValMat[i,j]	A p-value of Mann-Whitney test for adjMat[i,j].
adjDiffMat[i,j]	A lower bound of confidence interval of mean difference for sortedGroupList[j] minus sortedGroupList[i] using methodType bootstrap.
adjBootMat[i,j]	One if adjDiffMat[i,j] is positive, otherwise, zero.
netDen	A network density of dominant-distribution network derived from adjMat.
gObj	An object of iGraph of a dominant-distribution network.

Author(s)

Chainarong Amornbunchornvej, [e-mail]

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)

EDOIF documentation built on March 28, 2021, 9:11 a.m.