ranking <- read.table("rankingRes.txt", header=TRUE)

pdf('boxplot-S1.pdf')
boxplot(ranking)
dev.off()

#Kendall's coefficient of concordance W (ranges from 0, no agreement, to 1,complete agreement)
kendall(t(ranking))


# Texture paire vs Judged Rank:

resTabl <- matrix(data = 0, nrow=10, ncol=10)
for (i in 1:nrow(ranking)){
    for (j in 1:10){
    	resTabl[ranking[i,j],j] = 1 + resTabl[ranking[i,j],j]
    }
}


# Mean rank:

mean <- c(0,0,0,0,0,0,0,0,0,0)
for (i in 1:10){
    for (j in 1:10){
    	 mean[i] = mean[i] + resTabl[j,i]*j
    }
    mean[i] = mean[i]/nrow(ranking)
}

# Proportions: (#rank - mean rank)/(#ranks-1)

proportions <- c(0,0,0,0,0,0,0,0,0,0)
for (i in 1:10){
    proportions[i]=(10-mean[i])/9
}

#Z scores: (normal distribution assomption => Thurstone's law of comparative judgement)


pdf('qqnorm-S1.pdf')
myquant = qqnorm(proportions)
dev.off()

shapiro.test(proportions)


pMean <- mean(proportions)
pSd <- sd(proportions)
zScores <- c(0,0,0,0,0,0,0,0,0,0)
for (i in 1:10){
    zScores[i] = (proportions[i]-pMean)/pSd
}

library(ade4)
pdf('intervalScale-S1.pdf')
sco.label(zScores, label=c("1","2","3","4","5","6","7","8","9","10"),pos.lab=0.2)
dev.off()

#Significance test between pairs: Wilcoxon signed-rank test

res <- matrix(data=0, nrow=10, ncol=10)
for (i in 1:9) {
  for (j in (i+1):10) {
	res[i,j] <- (wilcox.test(array(ranking[,i]),array(ranking[,j]),var.equal=FALSE,paired=TRUE,mu=0,alternative="two.sided",conflevel=0.95))$p.value
  }
}