#Donnees

list(d=c(0 , 80 , 120 , 160 , 200 , 240 , 280 ), N=8, T=7,y=structure(	.Data = c(53.2 , NA , 39.4 , 36.1 , 48 , 69.3 , 66.2 , 19 , NA , 41.3 , 50.7 , 30.2 , 44.5 , 67.3 , 43.3 , NA , 53 , 50.2 , 62.3 , 55 , 67.3 , 29 , 31 , 34 , 39 , 45 , 44 , 44 , 41 , 41 , 40 , 40 , 40 , 45 , 53 , 50 , 45 , 51 , 59 , 59 , 71 , 71 , 40 , 47 , 44 , 61 , 55 , 63 , 76 , 44.5, NA , 52 , 55.1 , 57.1 , 66 , 83.6 ),	.Dim=c(8, 7)))

#Initialisation

list(Rmin=c(20,20,20,20,20,20,20,20), A=c(0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5), Xmin=c(140,140,140,140,140,140,140,140),Rmin.m=20,A.m=0.5,Xmin.m=140,var.Rmin.p=1,var.A.p=1,var.Xmin.p=10,var=1)



#Modele

model

# y= Reliquat N r?colte
# d = dose d'engrais
# alpha=param?tres de la r?gression
# prec=1/variance 

{     

for (i in 1:N) 

{		

for (j in 1:T) 

{         
 y[i,j] ~ dnorm(mu[i,j],prec)        
 mu[i,j]<-Rmin[i]+step(d[j]-Xmin[i])*A[i]*(d[j]-Xmin[i])     
                     }		

Rmin[i]~dnorm(Rmin.m,Rmin.p)		
A[i]~dnorm(A.m,A.p)		
Xmin[i]~dnorm(Xmin.m,Xmin.p)		

}		   

##Prior##               

Rmin.m~dnorm(20,1.0E-3)          
A.m~dnorm(0.5,1.0E-1)		
Xmin.m~dnorm(140,1.0E-4)				
var.Rmin.p~dunif(0,1000)		
var.A.p~dunif(0,100)		
var.Xmin.p~dunif(0,10000)		
var~dunif(0,10000)						
Rmin.p<-1/var.Rmin.p		
A.p<-1/var.A.p		
Xmin.p<-1/var.Xmin.p		
prec<-1/var			

}