CDOoDocuments.StdDocumentDescDocuments.DocumentDescContainers.ViewDescViews.ViewDescStores.StoreDescU/Documents.ModelDescContainers.ModelDescModels.ModelDescStores.ElemDesc. .TextViews.StdViewDescTextViews.ViewDescE.TextModels.StdModelDescTextModels.ModelDescO,G,bTextModels.AttributesDesc1$Courier New $Courier New) !) ) ) ) %) ' #------------------------------------------------------------------------------------------------------------------------------ Model 1: normal #------------------------------------------------------------------------------------------------------------------------------ model{ # model's likelihood for (i in 1:n){ time[i] ~ dnorm( mu[i], tau ) # stochastic componenent # link and linear predictor mu[i] <- beta0 + beta1 * cases[i] + beta2 * distance[i] } # prior distributions tau ~ dgamma( 0.01, 0.01 ) beta0 ~ dnorm( 0.0, 1.0E-4) beta1 ~ dnorm( 0.0, 1.0E-4) beta2 ~ dnorm( 0.0, 1.0E-4) # definition of sigma s2<-1/tau s <-sqrt(s2) # Expected y for a typical delivery time typical.y <- beta0 + beta1 * mean(cases[]) + beta2 * mean(distance[]) # # posterior probabilities of positive beta's p.beta0 <- step( beta0 ) p.beta1 <- step( beta1 ) p.beta2 <- step( beta2 ) } INITS list( tau=1, beta0=1, beta1=0, beta2=0 ) DATA (LIST) list( n=25, time = c(16.68, 11.5, 12.03, 14.88, 13.75, 18.11, 8, 17.83, 79.24, 21.5, 40.33, 21, 13.5, 19.75, 24, 29, 15.35, 19, 9.5, 35.1, 17.9, 52.32, 18.75, 19.83, 10.75), distance = c(560, 220, 340, 80, 150, 330, 110, 210, 1460, 605, 688, 215, 255, 462, 448, 776, 200, 132, 36, 770, 140, 810, 450, 635, 150), cases = c( 7, 3, 3, 4, 6, 7, 2, 7, 30, 5, 16, 10, 4, 6, 9, 10, 6, 7, 3, 17, 10, 26, 9, 8, 4) ) #------------------------------------------------------------------------------------------------------------------------------ Model 2: log-normal #------------------------------------------------------------------------------------------------------------------------------ model{ # model's likelihood for (i in 1:n){ #y[i] <- log( time[i] ) #y[i] ~ dnorm( mu[i], tau ) # stochastic componenent time[i] ~ dlnorm( mu[i], tau ) # stochastic componenent # link and linear predictor mu[i] <- beta0 + beta1 * cases[i] + beta2 * distance[i] } # prior distributions tau ~ dgamma( 0.01, 0.01 ) beta0 ~ dnorm( 0.0, 1.0E-4) beta1 ~ dnorm( 0.0, 1.0E-4) beta2 ~ dnorm( 0.0, 1.0E-4) # definition of sigma s2<-1/tau s <-sqrt(s2) # # posterior probabilities of positive beta's p.beta0 <- step( beta0 ) p.beta1 <- step( beta1 ) p.beta2 <- step( beta2 ) expbeta[1]<-exp(beta0) expbeta[2]<-exp(beta1) expbeta[3]<-exp(100*beta2) typical.y <- exp(beta0 + beta1 * mean(cases[]) + beta2 * mean(distance[])) } INITS list( tau=1, beta0=1, beta1=0, beta2=0 ) DATA (LIST) list( n=25, time = c(16.68, 11.5, 12.03, 14.88, 13.75, 18.11, 8, 17.83, 79.24, 21.5, 40.33, 21, 13.5, 19.75, 24, 29, 15.35, 19, 9.5, 35.1, 17.9, 52.32, 18.75, 19.83, 10.75), distance = c(560, 220, 340, 80, 150, 330, 110, 210, 1460, 605, 688, 215, 255, 462, 448, 776, 200, 132, 36, 770, 140, 810, 450, 635, 150), cases = c( 7, 3, 3, 4, 6, 7, 2, 7, 30, 5, 16, 10, 4, 6, 9, 10, 6, 7, 3, 17, 10, 26, 9, 8, 4) ) ----------------------------------------------- Model 3: gamma #------------------------------------------------------------------------------------------------------------------------------ model{ # model's likelihood for (i in 1:n){ time[i] ~ dgamma( a[i], tau ) # stochastic componenent # log-link and linear predictor a[i] <- mu[i]*tau log(mu[i]) <- beta0 + beta1 * cases[i] + beta2 * distance[i] } # prior distributions tau ~ dgamma( 0.01, 0.01 ) beta0 ~ dnorm( 0.0, 1.0E-4) beta1 ~ dnorm( 0.0, 1.0E-4) beta2 ~ dnorm( 0.0, 1.0E-4) # definition of sigma s2<-1/tau s <-sqrt(s2) # Expected y for a typical delivery time # # posterior probabilities of positive beta's p.beta0 <- step( beta0 ) p.beta1 <- step( beta1 ) p.beta2 <- step( beta2 ) expbeta[1]<-exp(beta0) expbeta[2]<-exp(beta1) expbeta[3]<-exp(100*beta2) typical.y <- exp(beta0 + beta1 * mean(cases[]) + beta2 * mean(distance[])) } INITS list( tau=1, beta0=1, beta1=0, beta2=0 ) DATA (LIST) list( n=25, time = c(16.68, 11.5, 12.03, 14.88, 13.75, 18.11, 8, 17.83, 79.24, 21.5, 40.33, 21, 13.5, 19.75, 24, 29, 15.35, 19, 9.5, 35.1, 17.9, 52.32, 18.75, 19.83, 10.75), distance = c(560, 220, 340, 80, 150, 330, 110, 210, 1460, 605, 688, 215, 255, 462, 448, 776, 200, 132, 36, 770, 140, 810, 450, 635, 150), cases = c( 7, 3, 3, 4, 6, 7, 2, 7, 30, 5, 16, 10, 4, 6, 9, 10, 6, 7, 3, 17, 10, 26, 9, 8, 4) ) ----------------------------------------------- Model 4: exponential #------------------------------------------------------------------------------------------------------------------------------ model{ # model's likelihood for (i in 1:n){ #time[i] ~ dgamma( 1, lambda[i] ) # stochastic componenent time[i] ~ dexp( lambda[i] ) # stochastic componenent # log-link and linear predictor log(lambda[i]) <- -(beta0 + beta1 * cases[i] + beta2 * distance[i]) } # prior distributions beta0 ~ dnorm( 0.0, 1.0E-4) beta1 ~ dnorm( 0.0, 1.0E-4) beta2 ~ dnorm( 0.0, 1.0E-4) # # posterior probabilities of positive beta's p.beta0 <- step( beta0 ) p.beta1 <- step( beta1 ) p.beta2 <- step( beta2 ) expbeta[1]<-exp(beta0) expbeta[2]<-exp(beta1) expbeta[3]<-exp(100*beta2) typical.y <- exp(beta0 + beta1 * mean(cases[]) + beta2 * mean(distance[])) } INITS list( tau=1, beta0=1, beta1=0, beta2=0 ) DATA (LIST) list( n=25, time = c(16.68, 11.5, 12.03, 14.88, 13.75, 18.11, 8, 17.83, 79.24, 21.5, 40.33, 21, 13.5, 19.75, 24, 29, 15.35, 19, 9.5, 35.1, 17.9, 52.32, 18.75, 19.83, 10.75), distance = c(560, 220, 340, 80, 150, 330, 110, 210, 1460, 605, 688, 215, 255, 462, 448, 776, 200, 132, 36, 770, 140, 810, 450, 635, 150), cases = c( 7, 3, 3, 4, 6, 7, 2, 7, 30, 5, 16, 10, 4, 6, 9, 10, 6, 7, 3, 17, 10, 26, 9, 8, 4) ) ----------------------------------------------- Model 5: chisq #------------------------------------------------------------------------------------------------------------------------------ model{ # model's likelihood for (i in 1:n){ time[i] ~ dgamma( a[i], 0.5 ) # stochastic componenent a[i] <- 0.5*mu[i] # time[i] ~ dchisqr( mu[i] ) # stochastic componenent # log-link and linear predictor log(mu[i]) <- beta0 + beta1 * cases[i] + beta2 * distance[i] } # prior distributions beta0 ~ dnorm( 0.0, 1.0E-4) beta1 ~ dnorm( 0.0, 1.0E-4) beta2 ~ dnorm( 0.0, 1.0E-4) # # posterior probabilities of positive beta's p.beta0 <- step( beta0 ) p.beta1 <- step( beta1 ) p.beta2 <- step( beta2 ) expbeta[1]<-exp(beta0) expbeta[2]<-exp(beta1) expbeta[3]<-exp(100*beta2) typical.y <- exp(beta0 + beta1 * mean(cases[]) + beta2 * mean(distance[])) } INITS list( tau=1, beta0=1, beta1=0, beta2=0 ) DATA (LIST) list( n=25, time = c(16.68, 11.5, 12.03, 14.88, 13.75, 18.11, 8, 17.83, 79.24, 21.5, 40.33, 21, 13.5, 19.75, 24, 29, 15.35, 19, 9.5, 35.1, 17.9, 52.32, 18.75, 19.83, 10.75), distance = c(560, 220, 340, 80, 150, 330, 110, 210, 1460, 605, 688, 215, 255, 462, 448, 776, 200, 132, 36, 770, 140, 810, 450, 635, 150), cases = c( 7, 3, 3, 4, 6, 7, 2, 7, 30, 5, 16, 10, 4, 6, 9, 10, 6, 7, 3, 17, 10, 26, 9, 8, 4) ) ----------------------------------------------- Model 6: igamma #------------------------------------------------------------------------------------------------------------------------------ model{ # model's likelihood for (i in 1:n){ y[i] <- 1/time[i] y[i] ~ dgamma( a[i], tau ) # stochastic componenent a[i] <- mu[i]*tau # log-link and linear predictor log(mu[i]) <- beta0 + beta1 * cases[i] + beta2 * distance[i] } # prior distributions tau ~ dgamma( 0.01, 0.01 ) beta0 ~ dnorm( 0.0, 1.0E-4) beta1 ~ dnorm( 0.0, 1.0E-4) beta2 ~ dnorm( 0.0, 1.0E-4) # definition of sigma s2<-1/tau s <-sqrt(s2) # calculation of the sample variance # # posterior probabilities of positive beta's p.beta0 <- step( beta0 ) p.beta1 <- step( beta1 ) p.beta2 <- step( beta2 ) expbeta[1]<-exp(beta0) expbeta[2]<-exp(beta1) expbeta[3]<-exp(100*beta2) typical.y <- exp(beta0 + beta1 * mean(cases[]) + beta2 * mean(distance[])) } INITS list( tau=1, beta0=1, beta1=0, beta2=0 ) DATA (LIST) list( n=25, time = c(16.68, 11.5, 12.03, 14.88, 13.75, 18.11, 8, 17.83, 79.24, 21.5, 40.33, 21, 13.5, 19.75, 24, 29, 15.35, 19, 9.5, 35.1, 17.9, 52.32, 18.75, 19.83, 10.75), distance = c(560, 220, 340, 80, 150, 330, 110, 210, 1460, 605, 688, 215, 255, 462, 448, 776, 200, 132, 36, 770, 140, 810, 450, 635, 150), cases = c( 7, 3, 3, 4, 6, 7, 2, 7, 30, 5, 16, 10, 4, 6, 9, 10, 6, 7, 3, 17, 10, 26, 9, 8, 4) ) Model 7: weibull #------------------------------------------------------------------------------------------------------------------------------ model{ # model's likelihood # prec is used to avoid overflows prec<-500 for (i in 1:n){ time[i] ~ dweib( v, lambda[i] ) # stochastic componenent #log(lambda[i])<- beta0 + beta1 * cases[i] + beta2 * distance[i] # if log.l>prec then lambda is set equal to exp(prec) lambda[i] <- exp( prec*(step(log.l[i]-prec))+ log.l[i]*(1-step(log.l[i]-prec)) ) log.l[i]<-v*loggam(1+1/v) - v*eta[i] eta[i] <- beta0 + beta1 * cases[i] + beta2 * distance[i] } # prior distributions v ~ dgamma( 0.01, 0.01 ) beta0 ~ dnorm( 0.0, 1.0E-4) beta1 ~ dnorm( 0.0, 1.0E-4) beta2 ~ dnorm( 0.0, 1.0E-4) # # posterior probabilities of positive beta's p.beta0 <- step( beta0 ) p.beta1 <- step( beta1 ) p.beta2 <- step( beta2 ) expbeta[1]<-exp(beta0) expbeta[2]<-exp(beta1) expbeta[3]<-exp(100*beta2) typical.y <- exp(beta0 + beta1 * mean(cases[]) + beta2 * mean(distance[])) } INITS list( v=1, beta0=1, beta1=0, beta2=0 ) DATA (LIST) list( n=25, time = c(16.68, 11.5, 12.03, 14.88, 13.75, 18.11, 8, 17.83, 79.24, 21.5, 40.33, 21, 13.5, 19.75, 24, 29, 15.35, 19, 9.5, 35.1, 17.9, 52.32, 18.75, 19.83, 10.75), distance = c(560, 220, 340, 80, 150, 330, 110, 210, 1460, 605, 688, 215, 255, 462, 448, 776, 200, 132, 36, 770, 140, 810, 450, 635, 150), cases = c( 7, 3, 3, 4, 6, 7, 2, 7, 30, 5, 16, 10, 4, 6, 9, 10, 6, 7, 3, 17, 10, 26, 9, 8, 4) ) TextControllers.StdCtrlDescTextControllers.ControllerDescContainers.ControllerDescControllers.ControllerDesc TextRulers.StdRulerDescTextRulers.RulerDescTextRulers.StdStyleDescTextRulers.StyleDescZTextRulers.AttributesDesc$ ZGo * ,[ @Documents.ControllerDesc t]s ' `h*