Tukey HSD groups represent in R

For the post hoc comparison of the ANOVA, R provide TukeyHSD for pairwise comparison and graphics function. For more informative represent the result, it's a good one from the SAS output. The following code do the same thing as SAS except the order is from minimul to maximum

	testResult <- function(x, ...) UseMethod("testResult")
	testResult.default <- function(x){
	if(! is(x,"aov")){
	return("Not an ANOVA test")
	}
	tmp <- unlist(summary(x))
	tmp1 <- tmp[which(names(tmp)=="Pr(>F)1", arr.ind=T)]
	mm <- model.tables(x, "means")
	tabs <- mm$tables[-1]
	gname <- dimnames(tabs[[names(tabs)]])
	mm1 <- unlist(mm)
	nn <- matrix(mm1[-1], ncol = 2)
	tmpsum <- data.frame(group = as.vector(gname), means = nn[,1], count = nn[,2])
	o <- order(tmpsum$means)
	tmpsum <- tmpsum[o,]
	if(tmp1 < 0.05){
	tmp <- TukeyHSD(x, order=T)
	tmp1 <- tmp[[names(tmp)]]
	tmp2 <- tmp1[,4]
	ng <- nrow(tmpsum)
	tmp.hsd <- matrix(1, ncol = ng, nrow = ng)
	tmp.hsd[lower.tri(tmp.hsd, diag = FALSE)] <- tmp2
	rownames(tmp.hsd) <- tmpsum$group
	for(col.i in 1:(ng-1)){
	tmp <- tmp.hsd[,col.i]
	ind <- which(tmp>0.05, arr.ind = TRUE)
	if(length(ind)>0){
	ind <- ind[ind > col.i-1]
	ind <- max(ind)
	} else {
	ind <- col.i
	}
	tmpsum$to[col.i] <- ind
	}
	tmpsum$to[ng] <- ng
	tmpsum$fr <- 1:nrow(tmpsum)
	nr <- unique(tmpsum$to)
	tmpsum$g <- as.character(" ")
	for(nr.ind in 1:length(nr)){
	tmp <- subset(tmpsum, to == nr[nr.ind], select = c("to","fr"))
	s.ind <- min(tmp$fr)
	e.ind <- max(tmp$to)
	if(nr.ind == 1){
	tmpsum$g[s.ind:e.ind] <- as.character(LETTERS[nr.ind])
	tmpsum$g[-(s.ind:e.ind)] <- as.character(" ")
	} else {
	tmpsum$g[s.ind:e.ind] <- paste(tmpsum$g[s.ind:e.ind], as.character(LETTERS[nr.ind]), sep="")
	tmpsum$g[-(s.ind:e.ind)] <- paste(tmpsum$g[-(s.ind:e.ind)], as.character(" "), sep="")
	}
	}
	out <- subset(tmpsum,, select=c("group","means","count","g"))
	} else {
	out <- data.frame(tmpsum, g = rep("A", nrow(tmpsum)))
	}
	return(out)
	}

view raw TukeyHSD SAS Like Output hosted with ❤ by GitHub