########################
##
## Patterns of measurement and available data
##
##	In the following function, audio and aes are two dataframes that contain the
##  the necessary variables. 
##
##  (I have a tendency to re-write this function to be specific to each longitudinal
##   data set)
##
########################

pagedisp <- function(p,audio,aes) {
	par (mgp=c(3,1,0),mar=c(5,5,4,2)+.1)
	plot (c(-12,24),c(1,length(p)),type="n",xlab="Time from Randomization (months)",
		xaxt="n",yaxt="n",ylab="Subject Identification Numbers")
	p <- p[p > 0 & p <= length(unique(audio$Subject))]
	title(paste("Information Status for Subjects",min(p),"-",max(p)))
	axis (1,c(-10,-6,0,3,6,9,12,15,21),labels=c("Rndmz","Days",0,3,6,9,12,15,"Reason"),cex.axis=0.8)
	for (i in 1:length(p)) {
		id <- unique(audio$Subject)[p][i]
		axis(2,i,id,las=1,tick=0,cex.axis=0.8)
		uAudio <- audio$Subject == id
		uAEs <- aes$Subject == id
		text(-10,i,format(unique(audio$StartDate[uAudio])),cex=0.5)
		text(-6,i,format(unique(audio$LengthTreatment[uAudio])),cex=0.5)
		if (unique(audio$Reason[uAudio]) != "complete")
		    text(21,i,format(unique(audio$Reason[uAudio])),cex=0.5)
		tAudio <- audio$VisitDays[uAudio] / 30.4
		tAEs <- aes$VisitDays[uAEs] / 30.4
		tTreat <- unique(audio$LengthTreatment[uAudio]) / 30.4
		tComb <- sort(unique(c(tAudio,tAEs)))
		lines(c(min(tComb),0),c(i,i),lty=3)
		lines(c(0,tTreat),c(i,i),lty=1)
		lines(c(tTreat,max(tComb)),c(i,i),lty=2)
		lbls <- c("N","A","M","B")[(tComb %in% tAudio) + 2 * (tComb %in% tAEs) + 1]
		cols <- c(1,2,5,2)[(tComb %in% tAudio) + 2 * (tComb %in% tAEs) + 1]
		text (tComb,rep(i-0.2,length(tComb)),lbls,cex=.5,col=cols)
	}
	abline(v=0,lty=3)
	abline(v=12,lty=3)
}
for (j in 1:5) pagedisp((1:25)+(j-1)*25)