#In order to implement the code, the data must be in two way table format.
#The data must be read into R using matrix(scan("data")).


#Function to compute the means of each level effect

sct.effects <- function(y){

n.dim <- dim(y)[1]
mean.diff <- c()
for (i in 1:n.dim){
mean.diff <- c(mean.diff,mean(y[,i]))

}

list(effects=mean.diff)

}


#Function for residual

sct.resid <- function(orig.matrix,col.effect,row.effect){

n.dim <- dim(orig.matrix)
median.matrix <- matrix(1,n.dim[1],n.dim[2])


for(i in 1:n.dim[2]){
orig.matrix[,i] <- orig.matrix[,i] - row.effect
}

for(j in 1:n.dim[1]){
orig.matrix[j,] <- orig.matrix[j,] - col.effect
}

grand.median <- median(orig.matrix)

median.matrix <- grand.median*median.matrix

orig.matrix <- orig.matrix - median.matrix

list(col=col.effect,row=row.effect,overall=grand.median,residual=orig.matrix)


}


#Function that computes results from SCT.

sct.fn <- function(x) {
temp.dim <- dim(x)

row.matrix <- matrix(0,temp.dim[1],temp.dim[1])
col.matrix <- matrix(0,temp.dim[2],temp.dim[2])

temp.coldiff <- c()
med.coldiff <- 0

n <- temp.dim[2]
m <- temp.dim[1]
for( i in 1:4 ) {
for( j in (i+1):5){
temp.coldiff <- x[,i]-x[,j]
med.coldiff <- median(temp.coldiff)
col.matrix[j,i] <- med.coldiff
}
}




for( i in 1:m-1 ) {
for( j in (i+1):m){
temp.coldiff <- x[i,]-x[j,]
med.coldiff <- median(temp.coldiff)
row.matrix[j,i] <- med.coldiff
}
}

col.matrix <- col.matrix + -1*t(col.matrix)
row.matrix <- row.matrix + -1*t(row.matrix)

row.effect <- sct.effects(row.matrix)$effects
col.effect <- sct.effects(col.matrix)$effects

sct.resid(x,col.effect,row.effect)

}