# Script generated by the UniPV WinBUGS converter
# 'WinBUGSConverter' copyright (c) BMS - University of Pavia (2016)
# Converter Version: 1.0.0
# Source: PharmML 0.8.1
# Target: WINBUGS 1.4.3
# Run ID: run_id_0
# Model: Generated from MDL. MOG ID: warfarin_PK_ODE_mog
# File: UseCase9
# Dated: Mon Aug 01 22:59:45 CEST 2016

model {
  for (ind_subj in 1:N_subj) {
    for (ind_t in 1:N_t[ind_subj]) {
      logtWTinterp[ind_subj,ind_t] <- interp.function(grid[ind_subj,ind_t], grid_logtWT[ind_subj,1:N_t_logtWT[ind_subj]], logtWT[ind_subj,1:N_t_logtWT[ind_subj]])
      Y_mean[ind_subj,ind_t] <- (CC[ind_subj,ind_t] + 0)
      Y_prec[ind_subj,ind_t] <- 1 / ((pow(((RUV_ADD + (RUV_PROP * CC[ind_subj,ind_t]))),2))*1)
      Y[ind_subj,ind_t] ~ dnorm(Y_mean[ind_subj,ind_t], Y_prec[ind_subj,ind_t])
      Y_res[ind_subj,ind_t] <- Y[ind_subj,ind_t]-Y_mean[ind_subj,ind_t]
      log(CL[ind_subj,ind_t]) <- log(POP_CL) + BETA_CL_WT * logtWTinterp[ind_subj,ind_t] + ETA_CL[ind_subj]
      log(V[ind_subj,ind_t]) <- log(POP_V) + BETA_V_WT * logtWTinterp[ind_subj,ind_t] + ETA_V[ind_subj]
    }
    ETA1[ind_subj,1:2] ~ dmnorm(mu1[], T1[,])
    ETA_CL[ind_subj] <- ETA1[ind_subj,1]
    ETA_V[ind_subj] <- ETA1[ind_subj,2]
    der99wb_unipv[ind_subj,1:N_t[ind_subj], 1:1]<- function.model1(grid[ind_subj,1:N_t[ind_subj]], amt[ind_subj,1:N_t[ind_subj]],  rate[ind_subj,1:N_t[ind_subj]], ii[ind_subj,1:N_t[ind_subj]], evid[ind_subj,1:N_t[ind_subj]], cmt[ind_subj,1:N_t[ind_subj]], addl[ind_subj,1:N_t[ind_subj]], ss[ind_subj,1:N_t[ind_subj]], theta[ind_subj,])
    theta[ind_subj,1] <- n_cov_cont
    theta[ind_subj,2] <- max_m_logtWT
    theta[ind_subj,3] <- N_t_logtWT[ind_subj]
    theta[ind_subj,3+2*max_m_logtWT +1] <- ETA_CL[ind_subj]
    theta[ind_subj,3+2*max_m_logtWT +2] <- POP_CL
    theta[ind_subj,3+2*max_m_logtWT +3] <- BETA_CL_WT
    theta[ind_subj,3+2*max_m_logtWT +4] <- ETA_V[ind_subj]
    theta[ind_subj,3+2*max_m_logtWT +5] <- POP_V
    theta[ind_subj,3+2*max_m_logtWT +6] <- BETA_V_WT
    for (i in 1 : max_m_logtWT){ 
	theta[ind_subj,i+3] <- grid_logtWT[ind_subj,i]
	}
	for (i in 1 : max_m_logtWT){ 
	theta[ind_subj,i+3+max_m_logtWT] <- logtWT[ind_subj,i]
	}
      theta[ind_subj,3+2*max_m_logtWT+7] <- 0.0 #IC CENTRAL
      theta[ind_subj,3+2*max_m_logtWT+8] <- 1 #F CENTRAL
      theta[ind_subj,3+2*max_m_logtWT+9] <- 0 #TLAG CENTRAL
      for (ind_q in 1:N_t[ind_subj]) {
        CENTRAL[ind_subj,ind_q] <- der99wb_unipv[ind_subj,ind_q,1] + 0.0
        CC[ind_subj,ind_q] <- (CENTRAL[ind_subj,ind_q] / V[ind_subj,ind_q])
      }
    }
    T1[1:2,1:2] <- inverse(OMEGA1[,])
    mu1[1] <- 0.0
    mu1[2] <- 0.0
    OMEGA1[1,1] <- pow(PPV_CL,2)
    OMEGA1[1,2] <- CORR_CL_V*PPV_CL*PPV_V
    OMEGA1[2,1] <- CORR_CL_V*PPV_CL*PPV_V
    OMEGA1[2,2] <- pow(PPV_V,2)
    MU_CL <- (log(0.1))
    MU_V <- (log(8))
    MU_PPV_CL <- (log(0.01))
    MU_PPV_V <- (log(0.3))
    VAR_CL <- 0.01
    VAR_V <- 0.09
    VAR_PPV_CL <- 1.0E-4
    VAR_PPV_V <- 0.001
    CORR_CL_V_MIN <- 0.1
    CORR_CL_V_MAX <- 0.2
    MU_RUV_PROP <- (log(0.1))
    MU_RUV_ADD <- (log(1.0E-4))
    VAR_RUV_PROP <- 0.1
    VAR_RUV_ADD <- 0.001
    CLmin <- 0.09
    CLmax <- 0.11
    BETA_CL_WT <- 0.75
    BETA_V_WT <- 1
    PPV_TLAG <- 1
    PPV_V <- 0.14
    PPV_KA <- 0.94
    RUV_PROP <- 0.11
    RUV_ADD <- 1.0E-4
    CORR_CL_V <- 0.28
    POP_KA <- 0.36
    PPV_CL <- 0.1
    POP_TLAG <- 1
    POP_V <- 8
    POP_CL <- 0.1
    # PRIORS
}
