In the first step, we generate a simple dataset. where C1 and C2 are dominated by C3, C3 is dominated by C4, and is C4 dominated by C5. There is no dominant-distribution relation between C1 and C2.
# Simulation section 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) )
The framework is used to analyze the data below.
# Simple ordering inference section library(EDOIF) # parameter setting bootT=1000 # Number of times of sampling with replacement alpha=0.05 # significance significance level #======= input Values=simData1$Values Group=simData1$Group #============= A1<-EDOIF(Values,Group,bootT = bootT, alpha=alpha )
We print the result of our framework below.
print(A1) # print results in text
The first plot is the plot of mean-difference confidence intervals
plot(A1,options =1)
The second plot is the plot of mean confidence intervals
plot(A1,options =2)
The third plot is a dominant-distribution network.
out<-plot(A1,options =3)
We generate more complicated dataset of mixture distributions. C1, C2, C3, and C4 are dominated by C5. There is no dominant-distribution relation among C1, C2, C3, and C4.
library(EDOIF) # parameter setting bootT=1000 alpha=0.05 nInv<-1200 start_time <- Sys.time() #======= input simData3<-SimNonNormalDist(nInv=nInv,noisePer=0.01) Values=simData3$Values Group=simData3$Group #============= A3<-EDOIF(Values,Group, bootT=bootT, alpha=alpha, methodType ="perc") A3 plot(A3) end_time <- Sys.time() end_time - start_time
Generating $A$ dominates $B$ with different degrees of uniform noise
library(ggplot2) nInv<-1000 simData3<-SimNonNormalDist(nInv=nInv,noisePer=0.01) #plot(density(simData3$V3)) dat <- data.frame(dens = c(simData3$V3, simData3$V5) , lines = rep(c("B", "A"), each = nInv)) #Plot. p1<-ggplot(dat, aes(x = dens, fill = lines)) + geom_density(alpha = 0.5) +xlim(-400, 400)+ ylim(0, 0.07) + ylab("Density [0,1]") +xlab("Values") + theme( axis.text.x = element_text(face="bold", size=12) ) theme_update(text = element_text(face="bold", size=12) ) p1$labels$fill<-"Categories" plot(p1)
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