Empirical Distribution Ordering Inference Framework (EDOIF)

Given a dataset of careers and incomes, how large a difference of income between any pair of careers would be? Given a dataset of travel time records, how long do we need to spend more when choosing a public transportation mode A instead of B to travel? In this work, we developed a framework to solve these problems named "EDOIF".

EDOIF is a nonparametric 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 a 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.

Installation

You can install our package from CRAN

r install.packages("EDOIF")

For the newest version on github, please call the following command in R terminal.

r remotes::install_github("DarkEyes/EDOIF") This requires a user to install the "remotes" package before installing EDOIF.

Example: Inferring orders of categories based on their empirical distributions

``` r library(EDOIF)

== simulation: Generating distributuions of five categories:

Category5 dominates Category4

Category4 dominates Category3

Category3 dominates Category2

Category2 dominates Category1

nInv=150 # number of samples per categories initMean=10 stepMean=20 std=8

simData1<-c() simData1$Values<-rnorm(nInv,mean=initMean,sd=std) simData1$Group<-rep(c("Category1"),times=nInv) simData1$Values<-c(simData1$Values,rnorm(nInv,mean=initMean,sd=std) ) simData1$Group<-c(simData1$Group,rep(c("Category2"),times=nInv)) simData1$Values<-c(simData1$Values,rnorm(nInv,mean=initMean+2stepMean,sd=std) ) simData1$Group<-c(simData1$Group,rep(c("Category3"),times=nInv) ) simData1$Values<-c(simData1$Values,rnorm(nInv,mean=initMean+3stepMean,sd=std) ) simData1$Group<-c(simData1$Group, rep(c("Category4"),times=nInv) ) simData1$Values<-c(simData1$Values,rnorm(nInv,mean=initMean+4*stepMean,sd=std) ) simData1$Group<-c(simData1$Group, rep(c("Category5"),times=nInv) )

== parameter setting

bootT=1000 # number of times of sample with replacement in bootstrap function. alpha=0.05 # Significance level

== Calling the class constructor

A1<-EDOIF(simData1$Values,simData1$Group, bootT=bootT, alpha=alpha, methodType ="perc")

== Visualizing results

print(A1) # print the results in text mode plot(A1, fontSize=10) # print the results in graphic mode ``` Graphic mode results 1. An alpha-confidence-interval of mean plot for five categories. The horizontal axis represents categories and the vertical axis represents values within distributions of categories.

2. A dominant-distribution network of five categories. A node represents categories and an edge represents a dominant-distribution relation between categories. If there is an edge from category A to B, then A dominates B. A larger node size implies a higher mean value of a category.

1. An alpha-confidence-interval of mean difference plot for five categories.

Text mode results

```

EDOIF (Empirical Distribution Ordering Inference Framework)

Alpha = 0.050000, Number of bootstrap resamples = 1000, CI type = perc Using Mann-Whitney test to report whether A ≺ B A dominant-distribution network density:0.900000 Distribution: Category1 Mean:10.840671 95CI:[ 9.706981,12.014179] Distribution: Category2 Mean:11.044785 95CI:[ 9.806991,12.446037] Distribution: Category3 Mean:50.462935 95CI:[ 49.208005,51.757706] Distribution: Category4 Mean:70.299726 95CI:[ 69.103924,71.502505] Distribution: Category5

Mean:91.190505 95CI:[ 89.895480,92.518455]

Mean difference of Category2 (n=150) minus Category1 (n=150): Category1 ⊀ Category2 :p-val 0.4463 Mean Diff:0.204114 95CI:[ -1.545130,1.930609]

Mean difference of Category3 (n=150) minus Category1 (n=150): Category1 ≺ Category3 :p-val 0.0000 Mean Diff:39.622264 95CI:[ 37.984831,41.378232]

Mean difference of Category4 (n=150) minus Category1 (n=150): Category1 ≺ Category4 :p-val 0.0000 Mean Diff:59.459055 95CI:[ 57.921328,61.127817]

Mean difference of Category5 (n=150) minus Category1 (n=150): Category1 ≺ Category5 :p-val 0.0000 Mean Diff:80.349835 95CI:[ 78.620391,82.133270]

Mean difference of Category3 (n=150) minus Category2 (n=150): Category2 ≺ Category3 :p-val 0.0000 Mean Diff:39.418150 95CI:[ 37.543210,41.241722]

Mean difference of Category4 (n=150) minus Category2 (n=150): Category2 ≺ Category4 :p-val 0.0000 Mean Diff:59.254941 95CI:[ 57.304359,61.098774]

Mean difference of Category5 (n=150) minus Category2 (n=150): Category2 ≺ Category5 :p-val 0.0000 Mean Diff:80.145720 95CI:[ 78.313321,82.040234]

Mean difference of Category4 (n=150) minus Category3 (n=150): Category3 ≺ Category4 :p-val 0.0000 Mean Diff:19.836791 95CI:[ 18.047421,21.762239]

Mean difference of Category5 (n=150) minus Category3 (n=150): Category3 ≺ Category5 :p-val 0.0000 Mean Diff:40.727570 95CI:[ 39.004372,42.627946]

Mean difference of Category5 (n=150) minus Category4 (n=150): Category4 ≺ Category5 :p-val 0.0000 Mean Diff:20.890780 95CI:[ 19.079287,22.625807]

``` For more examples, please see the vignettes in this link .

Citation

Amornbunchornvej, Chainarong, Navaporn Surasvadi, Anon Plangprasopchok, Suttipong Thajchayapong. "A nonparametric framework for inferring orders of categorical data from category-real pairs." Heliyon 6, no. 11 (2020): e05435, ISSN 2405-8440, https://doi.org/10.1016/j.heliyon.2020.e05435. arXiv

Contact

• Developer: C. Amornbunchornvej
  https://orcid.org/0000-0003-3131-0370
• Strategic Analytics Networks with Machine Learning and AI (SAI), NECTEC, Thailand
• Homepage: Link