model {
	
			for( i in 1 : N ) {
			   p[i] ~ dbeta(alpha,beta)
			   r[i] ~ dbin(p[i],n[i])
					}
			   ppred ~ dbeta(alpha,beta)		
		    # a priori sur la pr?valence moyenne g?n?rale 
			   p.mean~dunif(0,1) # prior sur la pr?valence moyenne g?n?rale	
			# a priori sur la taille de l'?chantillon qui aurait produit cette moyenne 
		       taille~dexp(0.001) # 1000 pr?l?vements en moy. (a priori unif(0,1) sur la pr?valence)
# a priori sur la correlation entre deux pr?levements d'une m?me ?tude
# rho~dbeta(1,1)
            # relation entre (p.mean, taille) et (alpha, beta) 
			    alpha<-p.mean*taille
                   beta<-(1-p.mean)*taille        
# relation entre (p.mean,rho) et (alpha,beta)
# alpha<-(1-rho)*p.mean/rho
# beta<-(1-rho)*(1-p.mean)/rho    
			}
			
list(N=91,
n=c(600,415,276,220,150,142,120,100,100,85,84,71,69,55,50,50,40,32,20,1227053,635,4046,340,169,964,317,445,134,256,252,939,187,59,426,100,100,100,98,190,560,540,1720,290,236,300,2511,123,177,361,350,561,80,150,100,50,50,292,337,48,137,589,220,100,2009,256,961,113,315,69,100,1409,80,200,81,50,445,30,100,124,121,640,176,97,77,16,51,17,14,16,95,21),
r=c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,278,2,14,2,1,9,4,6,2,4,4,15,3,1,8,2,2,2,2,4,14,14,47,8,7,9,79,4,6,13,13,21,3,6,4,2,2,12,14,2,6,29,11,5,102,13,50,6,17,4,6,85,5,14,6,4,38,3,12,15,15,90,27,15,14,3,10,5,6,7,43,17))