CDOoDocuments.StdDocumentDescDocuments.DocumentDescContainers.ViewDescViews.ViewDescStores.StoreDesc8Documents.ModelDescContainers.ModelDescModels.ModelDescStores.ElemDesc ~TextViews.StdViewDescTextViews.ViewDesc(TextModels.StdModelDescTextModels.ModelDescTextModels.AttributesDesc1$Courier New  ;$Courier New 7 .**uTTextRulers.StdRulerDescTextRulers.RulerDescTextRulers.StdStyleDescTextRulers.StyleDescTextRulers.AttributesDescL Zo Z%$.6?HLQ*uTgL Zo Z%$.6?HLQ B*:~*uTgL Zo Z%$.6?HLQ*uTgL Zo Z%$.6?HLQ:*uTgL Zo Z%$.6?HLQ:#------------------------------------------------------------------------------------------------------------------------------ # Alligators data (revisited) # Data takes from Agrest (2002), page 304, Table 7.16, problem 7.4 #------------------------------------------------------------------------------------------------------------------------------ Model 3: USING SEPARATE LOGISTIC MODELS #------------------------------------------------------------------------------------------------------------------------------ model{ # create multinomial data for (i in 1:n){ for (k in 1:K){ y[i,k] <- equals( choice[i],k) } } # model's likelihood for (i in 1:n){ for( k in 1:K ) { # linear predictors eta[i,k] <- beta[1,k] + beta[2,k]*size[i] + beta[3,k]*gender[i] N.star[i,k] <- y[i,1] + y[i,k] } for( k in 1:K ) { # link logit(p[i,k]) <- eta[i,k]*equals(N.star[i,k],1)-500*(1-equals(N.star[i,k],1)) # stochastic part y[i,k] ~ dbin( p[i,k], 1 ) } } # for (j in 1:P){ # coefficients for the baseline category are constrained to zero beta[j,1] <- 0.0 # independent normal low information priors for (k in 2:K){ beta[j,k] ~ dnorm( 0.0, 0.001) } } } INITS list( beta=structure(.Data=c(NA, 0, 0, NA, 0, 0, NA, 0, 0), .Dim = c(3, 3))) # # choice 1= FISH,2=INVERTEBRATE, 3=OTHER # gender 1=MALE, 0=FEMALE DATA (LIST) list( n=63, K=3, 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, 1.65, 1.65, 1.68, 1.7, 1.73, 1.78, 1.78, 1.8, 1.85, 1.93, 1.93, 1.98, 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, 1.24, 1.3, 1.45, 1.45, 1.55, 1.6, 1.6, 1.65, 1.78, 1.78, 1.8, 1.88, 2.16, 2.26, 2.31, 2.36, 2.39, 2.41, 2.44, 2.56, 2.67, 2.72, 2.79, 2.84), choice = c(2, 1, 1, 1, 2, 1, 2, 1, 2, 2, 2, 3, 3, 2, 1, 1, 2, 3, 1, 3, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 3, 3, 1, 1, 1, 1, 3, 1, 1, 2, 2, 2, 3, 2, 2, 2, 1, 2, 3, 2, 2, 1, 1, 1, 1, 1, 1, 1, 3, 1, 2, 1, 1), 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, 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, 0, 0, 0) )  node mean sd MC error 2.5% median 97.5% start sample beta[1,2] 7.414 2.277 0.08875 3.374 7.253 12.31 5001 40000 beta[1,3] -1.088 1.386 0.03148 -3.857 -1.089 1.681 5001 40000 beta[2,2] -3.685 1.086 0.04213 -6.003 -3.608 -1.745 5001 40000 beta[2,3] -0.148 0.5206 0.01147 -1.226 -0.1332 0.843 5001 40000 beta[3,2] -1.736 0.8592 0.02108 -3.535 -1.702 -0.156 5001 40000 beta[3,3] 0.1819 0.8297 0.01001 -1.392 0.1588 1.885 5001 40000 results from 03_binomial2   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*