# This script uses functions in the packages "irr" and "MASS"
# The following script performs Schools of Thought Analysis on ranks 
# provided by the judges

f <- file.choose()                    #get filename
rr <- read.csv(f,row.names=1)         #read file of judges' raw ranks
sr <- apply(rr,MARGIN=2,rank)         #scale to canonical form
rs <- rowSums(sr)                     #calculate rank sum
borda <- rank(rs)                     #generate ranks for borda
sr <- sr[,order(-cor(sr,borda,method="kendall"))]   
                                      #reorder by agreement with borda
srb <- cbind(sr,borda)[order(borda),] #bind borda to judges' ranks
w <- kendall(sr,correct=TRUE)         #determine coeff concordance
tau <- cor(srb,method="kendall")      #calculate tau matrix
d <- as.dist((1-tau)/2)               #calculate distances
ca <- hclust(d,method="average")      #determine cluster config
mds <- cmdscale(d)                    #determine mds config
x <- mds[,1]                          #extract x coord for mds
y <- mds[,2]                          #extract y coord for mds
plot(ca,ylab="distance")              #plot hierarchical clusters
plot (x,y,type="n",xlab="",ylab="",xlim=range(x)*1.2,
      ylim=range(y)*1.2)              #set frame for mds map
text(x,y,rownames(mds))               #add judge names to mds map

# The following script performs Schools of Thought Analysis on results
# from the Analytic Hierarchy Process. It then converts the AHP scores to
# ranks and performs SOTA again using those ranks. 

f <- file.choose()                       #get filename
ahp <- read.csv(f,row.names=1)           #read file of ahp scores
group <- ahp[ ,length(ahp)]              #get group scores only (last column)
ahps <- ahp[ ,-length(ahp)]              #get participant ahp scores only
ahps <- ahps[ ,order(-cor(ahps,group))]  #reorder by agreement with group
ord <- cbind(ahps,group)[order(-group),] #bind participants scores to group 
pcor <- cor(ord)                         #calculate pearson cor matrix
d <- as.dist((1-pcor)/2)                 #calculate distances
ca <- hclust(d,method="average")         #determine cluster config
mds <- cmdscale(d)                       #determine mds config
x <- mds[,1]                             #extract x coord for mds
y <- mds[,2]                             #extract y coord for mds
plot(ca,ylab="distance")                 #plot hierarchical clusters
plot (x,y,type="n",xlab="",ylab="",xlim=range(x)*1.2,
      ylim=range(y)*1.2)                 #set frame for mds map
text(x,y,rownames(mds))                  #add judge names to mds map
#repeat Schools of Thought Analysis with ahp scores converted to rankings
sr <- apply(-ahps,MARGIN=2,rank)         #scale to canonical form
rs <- rowSums(sr)                        #calculate rank sum
borda <- rank(rs)                        #generate ranks for borda
sr <- sr[,order(-cor(sr,borda,method="kendall"))]   
                                         #reorder by agreement with borda
srb <- cbind(sr,borda)[order(borda),]    #bind borda to judges' ranks
w <- kendall(sr,correct=TRUE)            #determine coeff concordance
tau <- cor(srb,method="kendall")         #calculate tau matrix
d <- as.dist((1-tau)/2)                  #calculate distances
ca <- hclust(d,method="average")         #determine cluster config
mds <- cmdscale(d)                       #determine mds config
x <- mds[,1]                             #extract x coord for mds
y <- mds[,2]                             #extract y coord for mds
plot(ca,ylab="distance")                 #plot hierarchical clusters
plot (x,y,type="n",xlab="",ylab="",xlim=range(x)*1.2,
      ylim=range(y)*1.2)                 #set frame for mds map
text(x,y,rownames(mds))                  #add judge names to mds map