########################################################## for (i in 1:N_subj) {for (t in 1:N_obs) {W[i,t]~dnorm(X[i,t],tau_eps) X[i,t]<-xi[i,1]*psi[t,1]+xi[i,2]*psi[t,2]+xi[i,3]*psi[t,3]+ xi[i,4]*psi[t,4]+xi[i,5]*psi[t,5]+xi[i,6]*psi[t,6]+ xi[i,7]*psi[t,7]+xi[i,8]*psi[t,8]+xi[i,9]*psi[t,9]+ xi[i,10]*psi[t,10]} for (k in 1:dim.space) {xi[i,k]~dnorm(0,tau_lambda[k])} } ########################################################## for (k in 1:dim.space) {tau_lambda[k]~dgamma(1.0E-3,1.0E-3) lambda[k]<-1/ll[k]} ########################################################## for (i in 1:N_subj) {Y[i]~dbern(pY[i]) logit(pY[i])<-mu+beta[1]*xi[i,1]+beta[2]*xi[i,2]+beta[3]*xi[i,3]} ########################################################## for (j in 1:3){beta[j]~dnorm(0,1.0E-2)} mu~dnorm(0,1.0E-2) ########################################################## logit(pY[i])<-mu[i] mu[i]<-mp+eta[i] eta[i]<-xi[i,1]*gamma[1]+xi[i,2]*gamma[2]+xi[i,3]*gamma[3]+ xi[i,4]*gamma[4]+xi[i,5]*gamma[5]+xi[i,6]*gamma[6]+ xi[i,7]*gamma[7]+xi[i,8]*gamma[8]+xi[i,9]*gamma[9]+ xi[i,10]*gamma[10] ########################################################## for(l in 1:L){ gamma[l]<-J[l,1]*beta[1]+J[l,2]*beta[2]+J[l,3]*beta[3]+ J[l,4]*beta[4]+J[l,5]*beta[5]+J[l,6]*beta[6]+ J[l,7]*beta[7]+J[l,8]*beta[8]+J[l,9]*beta[9]+ J[l,10]*beta[10] } ########################################################## for (l in 2:L) {beta[l]~dnorm(beta[l-1],taubeta)} beta[1]~dnorm(0,1.0E-6) taubeta~dgamma(1.0E-3,1.0E-3) ########################################################## for (i in 1:N_subj) {xi[i,1]~dnorm(m_xi[i],ll[1]) m_xi[i]<-mu+gamma[1]*age[i]+gamma[2]*BMI[i]} ########################################################## for (i in 1:N_subj) {age[i]~dnorm(mu_X[1],tau[1]) BMI[i]~dnorm(mu_X[2],tau[2])} ########################################################## mu~dnorm(0,1.0E-2) for (l in 1:2) {gamma[l]~dnorm(0,1.0E-2) mu_X[l]~dnorm(m_prior[l],1.0E-3) tau[l]~dgamma(1.0E-3,1.0E-3)} ########################################################## for (i in 1:N_subj) {for (t in 1:N_obs) {W_1[i,t]~dnorm(m_1[i,t],taueps) W_2[i,t]~dnorm(m_2[i,t],taueps) m_1[i,t]<-X[i,t]+U_1[i,t] m_2[i,t]<-X[i,t]+U_2[i,t] X[i,t]<-xi[i,1]*psi_1[t,1]+xi[i,2]*psi_1[t,2]+xi[i,3]*psi_1[t,3] U_1[i,t]<-zi[i,1,1]*psi_2[t,1]+zi[i,2,1]*psi_2[t,2]+ zi[i,3,1]*psi_2[t,3]+zi[i,4,1]*psi_2[t,4]+zi[i,5,1]*psi_2[t,5]+ zi[i,6,1]*psi_2[t,6]+zi[i,7,1]*psi_2[t,7]+zi[i,8,1]*psi_2[t,8]+ zi[i,9,1]*psi_2[t,9]+zi[i,10,1]*psi_2[t,10] U_2[i,t]<-zi[i,1,2]*psi_2[t,1]+zi[i,2,2]*psi_2[t,2]+ zi[i,3,2]*psi_2[t,3]+zi[i,4,2]*psi_2[t,4]+zi[i,5,2]*psi_2[t,5]+ zi[i,6,2]*psi_2[t,6]+zi[i,7,2]*psi_2[t,7]+zi[i,8,2]*psi_2[t,8]+ zi[i,9,2]*psi_2[t,9]+zi[i,10,2]*psi_2[t,10] } for (k in 1:dim.space_b) {xi[i,k]~dnorm(0,ll_b[k])} for (l in 1:dim.space_w) {zi[i,l,1]~dnorm(0,ll_w[l]) zi[i,l,2]~dnorm(0,ll_w[l])} } ########################################################## for (i in 1:dim.space_b) {ll_b[i]~dgamma(1.0E-3,1.0E-3) lambda_b[i]<-1/ll_b[i]} for (i in 1:dim.space_w) {ll_w[i]~dgamma(1.0E-3,1.0E-3) lambda_w[i]<-1/ll_w[i]} taueps~dgamma(1.0E-3,1.0E-3) sigma_sq_eps=1/taueps ########################################################## model { for (i in 1:N_subj) {#Begin loop over subjects for (t in 1:N_obs) {#Begin loop over observations within subjects W[i,t]~dnorm(m[i,t],taueps) m[i,t]<-xi[i,1]*E[t,1]+xi[i,2]*E[t,2]+xi[i,3]*E[t,3]+ xi[i,4]*E[t,4]+xi[i,5]*E[t,5]+xi[i,6]*E[t,6]+ xi[i,7]*E[t,7]+xi[i,8]*E[t,8]+xi[i,9]*E[t,9]+ xi[i,10]*E[t,10] }#End loop over observations within subjects for (k in 1:dim.space) {#Begin loop over the eigenfunctions xi[i,k]~dnorm(0,ll[k]) }#End loop over the eigenfunctions }#End loop over subjects for (k in 1:dim.space) {ll[k]~dgamma(1.0E-3,1.0E-3) lambda[k]<-1/ll[k]} taueps~dgamma(1.0E-3,1.0E-3) } ########################################################## model { for (i in 1:N_subj) {#Begin loop over subjects Y[i]~dbern(pY[i]) logit(pY[i])<-mp+beta[1]*xi[i,1]+beta[2]*xi[i,2]+beta[3]*xi[i,3] for (t in 1:N_obs) {#Begin loop over observations within subjects W[i,t]~dnorm(m[i,t],taueps) m[i,t]<-xi[i,1]*E[t,1]+xi[i,2]*E[t,2]+xi[i,3]*E[t,3]+ xi[i,4]*E[t,4]+xi[i,5]*E[t,5]+xi[i,6]*E[t,6]+ xi[i,7]*E[t,7]+xi[i,8]*E[t,8]+xi[i,9]*E[t,9]+ xi[i,10]*E[t,10] }#End loop over observations within subjects for (j in 1:dim.space) {#Begin loop over the eigenfunctions xi[i,j]~dnorm(0,ll[j]) }#End loop over the eigenfunctions }# for (i in 1:dim.space) {ll[i]~dgamma(1.0E-3,1.0E-3) lambda[i]<-1/ll[i]} for (l in 1:3) {beta[l]~dnorm(0,1.0E-2)} mp~dnorm(0,1.0E-2) taueps~dgamma(1.0E-3,1.0E-3) } ########################################################## model { for (i in 1:N_subj) {#Begin loop over subjects Y[i]~dbern(pY[i]) logit(pY[i])<-mp+eta[i] eta[i]<-xi[i,1]*gamma[1]+xi[i,2]*gamma[2]+xi[i,3]*gamma[3]+ xi[i,4]*gamma[4]+xi[i,5]*gamma[5]+xi[i,6]*gamma[6]+ xi[i,7]*gamma[7]+xi[i,8]*gamma[8]+xi[i,9]*gamma[9]+ xi[i,10]*gamma[10] for (t in 1:N_obs) {#Begin loop over observations within subjects W[i,t]~dnorm(m[i,t],taueps) m[i,t]<-xi[i,1]*E[t,1]+xi[i,2]*E[t,2]+xi[i,3]*E[t,3]+ xi[i,4]*E[t,4]+xi[i,5]*E[t,5]+xi[i,6]*E[t,6]+ xi[i,7]*E[t,7]+xi[i,8]*E[t,8]+xi[i,9]*E[t,9]+ xi[i,10]*E[t,10] }# End loop over observations within subjects for (j in 1:dim.space) {#Begin loop over eigenfunctions xi[i,j]~dnorm(0,ll[j]) }#End loop over eigenfunctions }#End loop over subjects for(l in 1:L){#Begin loop rows of J gamma[l]<-J[l,1]*beta[1]+J[l,2]*beta[2]+J[l,3]*beta[3]+ J[l,4]*beta[4]+J[l,5]*beta[5]+J[l,6]*beta[6]+ J[l,7]*beta[7]+J[l,8]*beta[8]+J[l,9]*beta[9]+ J[l,10]*beta[10] }#End loop over rows of J for (i in 1:dim.space) {ll[i]~dgamma(1.0E-3,1.0E-3) lambda[i]<-1/ll[i]} for (i in 2:L) {beta[l]~dnorm(beta[l-1],taubeta)} beta[1]~dnorm(0,1.0E-6) taubeta~dgamma(1.0E-3,1.0E-3) mp~dnorm(0,1.0E-2) taueps~dgamma(1.0E-3,1.0E-3) } ########################################################## model { for (i in 1:N_subj) {#Begin loop over subjects xi[i,1]~dnorm(m_xi[i],ll[1]) m_xi[i]<-mu+gamma[1]*age[i]+gamma[2]*BMI[i] age[i]~dnorm(mu_X[1],tau[1]) BMI[i]~dnorm(mu_X[2],tau[2]) for (t in 1:N_obs) {#Begin loop over observations within subjects W[i,t]~dnorm(m[i,t],taueps) m[i,t]<-xi[i,1]*E[t,1]+xi[i,2]*E[t,2]+xi[i,3]*E[t,3]+ xi[i,4]*E[t,4]+xi[i,5]*E[t,5]+xi[i,6]*E[t,6]+ xi[i,7]*E[t,7]+xi[i,8]*E[t,8]+xi[i,9]*E[t,9]+ xi[i,10]*E[t,10] }#End loop over observations within subjects for (k in 2:dim.space) {#Begin loop over scores that are not regressed xi[i,k]~dnorm(0,ll[k]) }#End loop over scores that are not regressed }#End loop over subjects for (k in 1:dim.space) {ll[k]~dgamma(1.0E-3,1.0E-3) lambda[k]<-1/ll[k]} for (l in 1:2) {#Begin loop over covariates gamma[l]~dnorm(0,1.0E-2) mu_X[l]~dnorm(m_prior[l],1.0E-3) tau[l]~dgamma(1.0E-3,1.0E-3) }#End loop over covariates mu~dnorm(0,1.0E-2) taueps~dgamma(1.0E-3,1.0E-3) } ########################################################## model { for (i in 1:N_subj) {#Begin loop over subjects for (t in 1:N_obs) {#Begin loop over observations within subjects #Define the distribution of visit 1 and 2 observations W_1[i,t]~dnorm(m_1[i,t],taueps) W_2[i,t]~dnorm(m_2[i,t],taueps) #Write the mean as the subject-specific mean plus #the subject-visit-specific deviation m_1[i,t]<-X[i,t]+U_1[i,t] m_2[i,t]<-X[i,t]+U_2[i,t] #Define the subject-specific mean structure X[i,t]<-xi[i,1]*psi_1[t,1]+xi[i,2]*psi_1[t,2]+xi[i,3]*psi_1[t,3] #Define the visit 1-specific deviation from the mean U_1[i,t]<-zi[i,1,1]*psi_2[t,1]+zi[i,2,1]*psi_2[t,2]+ zi[i,3,1]*psi_2[t,3]+zi[i,4,1]*psi_2[t,4]+zi[i,5,1]*psi_2[t,5]+ zi[i,6,1]*psi_2[t,6]+zi[i,7,1]*psi_2[t,7]+zi[i,8,1]*psi_2[t,8]+ zi[i,9,1]*psi_2[t,9]+zi[i,10,1]*psi_2[t,10] #Define the visit 2-specific deviation from the mean U_2[i,t]<-zi[i,1,2]*psi_2[t,1]+zi[i,2,2]*psi_2[t,2]+ zi[i,3,2]*psi_2[t,3]+zi[i,4,2]*psi_2[t,4]+zi[i,5,2]*psi_2[t,5]+ zi[i,6,2]*psi_2[t,6]+zi[i,7,2]*psi_2[t,7]+zi[i,8,2]*psi_2[t,8]+ zi[i,9,2]*psi_2[t,9]+zi[i,10,2]*psi_2[t,10] } for (k in 1:dim.space_b) {xi[i,k]~dnorm(0,ll_b[k])} for (l in 1:dim.space_w) {zi[i,l,1]~dnorm(0,ll_w[l]) zi[i,l,2]~dnorm(0,ll_w[l])} }# for (k in 1:dim.space_b) {ll_b[k]~dgamma(1.0E-3,1.0E-3) lambda_b[k]<-1/ll_b[k]} for (l in 1:dim.space_w) {ll_w[l]~dgamma(1.0E-3,1.0E-3) lambda_w[l]<-1/ll_w[l]} taueps~dgamma(1.0E-3,1.0E-3) sigma_sq_eps<-1/taueps } ##########################################################