CDOoDocuments.StdDocumentDescDocuments.DocumentDescContainers.ViewDescViews.ViewDescStores.StoreDesc2Documents.ModelDescContainers.ModelDescModels.ModelDescStores.ElemDesc xTextViews.StdViewDescTextViews.ViewDesc"TextModels.StdModelDescTextModels.ModelDesc  +TextModels.AttributesDesc1$Courier New ;$Courier NewK> .**uTTextRulers.StdRulerDescTextRulers.RulerDescTextRulers.StdStyleDescTextRulers.StyleDescTextRulers.AttributesDescL Zo Z%$.6?HLQ *:|#------------------------------------------------------------------------------------------------------------------------------ # Alligators data (revisited) # Data takes from Agrest (2002), page 304, Table 7.16, problem 7.4 #------------------------------------------------------------------------------------------------------------------------------ # Model 5: USING SEPARATE conditional LOGISTIC MODELS # FITS TWO MODELS SIMULTANEOUSLY # 2 vs 1+3 # 1 vs 3 # the result is the same as the multinomial model #------------------------------------------------------------------------------------------------------------------------------ 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] } logit(p[i,1]) <- 0.0 logit(p[i,2]) <- eta[i,2] logit(p[i,3]) <- eta[i,3]*equals(N.star[i,3],1)-500*(1-equals(N.star[i,3],1)) for( k in 1:K ) { # link # 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] 6.046 1.933 0.08012 2.598 5.94 10.12 5001 30000 beta[1,3] -1.116 1.387 0.0359 -3.877 -1.118 1.58 5001 30000 beta[2,2] -3.24 0.9706 0.03998 -5.325 -3.181 -1.518 5001 30000 beta[2,3] -0.1374 0.5233 0.01362 -1.205 -0.1261 0.8763 5001 30000 beta[3,2] -1.355 0.7272 0.0164 -2.845 -1.339 0.01994 5001 30000 beta[3,3] 0.1831 0.8331 0.01095 -1.395 0.158 1.895 5001 30000 TextControllers.StdCtrlDescTextControllers.ControllerDescContainers.ControllerDescControllers.ControllerDesc aY?$ ZGo * ,[ @Documents.ControllerDesc t]s ' `h*