CDOoDocuments.StdDocumentDescDocuments.DocumentDescContainers.ViewDescViews.ViewDescStores.StoreDescDocuments.ModelDescContainers.ModelDescModels.ModelDescStores.ElemDesc TextViews.StdViewDescTextViews.ViewDescTextModels.StdModelDescTextModels.ModelDesc'TextModels.AttributesDesc1$Courier New ;$Courier Newf G*b +*uTTextRulers.StdRulerDescTextRulers.RulerDescTextRulers.StdStyleDescTextRulers.StyleDescTextRulers.AttributesDescL Zo Z%$.6?HLQ 8*:*uTgL Zo Z%$.6?HLQ:#------------------------------------------------------------------------------------------------------------------------------ # Alligators data (revisited) # Data takes from Agrest (2002), page 304, Table 7.16, problem 7.4 #------------------------------------------------------------------------------------------------------------------------------ Model 4: separate binomials #------------------------------------------------------------------------------------------------------------------------------ model{ # model's likelihood for (i in 1:n){ # linear predictor eta[i] <- beta[1] + beta[2]*size[i] + beta[3]*gender[i] logit(p[i]) <- eta[i] choice[i] ~ dbin( p[i], 1 ) } # for (j in 1:P){ # coefficients for the baseline category are constrained to zero beta[j] ~ dnorm( 0.0, 0.001) } } list( beta=c(0,0,0) ) # CHOICE 2 vs 1 (INVERTEBRATE vs. FISH) # choice 1= INVERTEBRATE,0=FISH # gender 1=MALE, 0=FEMALE DATA1 (LIST) list( n=53, P=3, size = c(1.3, 1.32, 1.32, 1.4, 1.42, 1.42, 1.47, 1.47, 1.5, 1.52, 1.63, 1.65, 1.65, 1.68, 1.7, 1.78, 1.8, 1.85, 1.93, 1.93, 1.98, 2.03, 2.03, 2.31, 2.36, 2.46, 3.33, 3.56, 3.58, 3.66, 3.71, 3.89, 1.24, 1.3, 1.45, 1.55, 1.6, 1.6, 1.65, 1.78, 1.8, 1.88, 2.16, 2.26, 2.31, 2.36, 2.39, 2.41, 2.44, 2.67, 2.72, 2.79, 2.84 ), choice = c(1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0), gender = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0) ) # CHOICE 2 vs 1 (OTHER vs. FISH) # choice 1= OTHER,0=FISH # gender 1=MALE, 0=FEMALE DATA2 (LIST) list( n=43, P=3, size = c(1.32, 1.32, 1.4, 1.42, 1.47, 1.65, 1.65, 1.65, 1.68, 1.73, 1.78, 1.78, 1.8, 1.85, 1.93, 2.03, 2.03, 2.31, 2.36, 2.46, 3.25, 3.28, 3.33, 3.56, 3.58, 3.66, 3.68, 3.71, 3.89, 1.45, 1.65, 1.78, 2.16, 2.26, 2.31, 2.36, 2.39, 2.41, 2.44, 2.56, 2.67, 2.79, 2.84), choice = c(0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0), gender = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0) )  node mean sd MC error 2.5% median 97.5% start sample beta[1] 7.275 2.262 0.08753 3.307 7.136 12.2 5001 40000 beta[2] -3.62 1.081 0.0413 -5.988 -3.546 -1.711 5001 40000 beta[3] -1.701 0.8456 0.02113 -3.455 -1.663 -0.1638 5001 40000  node mean sd MC error 2.5% median 97.5% start sample beta[1] -1.128 1.359 0.02964 -3.807 -1.124 1.538 5001 40000 beta[2] -0.1352 0.5103 0.01099 -1.166 -0.1233 0.8235 5001 40000 beta[3] 0.1981 0.8318 0.009097 -1.369 0.1784 1.914 5001 40000 TextControllers.StdCtrlDescTextControllers.ControllerDescContainers.ControllerDescControllers.ControllerDesc aY?$ ZGo * ,[ @Documents.ControllerDesc t]s ' `h*