CDOoDocuments.StdDocumentDescDocuments.DocumentDescContainers.ViewDescViews.ViewDescStores.StoreDescDocuments.ModelDescContainers.ModelDescModels.ModelDescStores.ElemDesc# TextViews.StdViewDescTextViews.ViewDescTextModels.StdModelDescTextModels.ModelDescH @ TextModels.AttributesDesc1$Courier New ;$Courier New7 .**uTTextRulers.StdRulerDescTextRulers.RulerDescTextRulers.StdStyleDescTextRulers.StyleDescTextRulers.AttributesDescL Zo Z%$.6?HLQ 3**uTgL Zo Z%$.6?HLQ:|#------------------------------------------------------------------------------------------------------------------------------ # Alligators data (revisited) # Data takes from Agrest (2002), page 304, Table 7.16, problem 7.4 #------------------------------------------------------------------------------------------------------------------------------ Model 2: USING MULTI(P,1) #------------------------------------------------------------------------------------------------------------------------------ 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] expeta[i,k]<-exp(eta[i,k]) # probabilities (link function) p[i,k] <- expeta[i,k]/sum(expeta[i,1:K]) } # stochastic part y[i,1:K] ~ dmulti( p[i,1: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] 6.312 1.888 0.08996 2.869 6.24 10.37 5001 20000 beta[1,3] -1.186 1.339 0.04606 -3.875 -1.171 1.419 5001 20000 beta[2,2] -3.245 0.9455 0.04444 -5.286 -3.209 -1.55 5001 20000 beta[2,3] -0.1173 0.501 0.01639 -1.113 -0.1109 0.852 5001 20000 beta[3,2] -1.305 0.7458 0.01957 -2.807 -1.291 0.1031 5001 20000 beta[3,3] 0.2198 0.8439 0.01656 -1.343 0.1786 1.984 5001 20000 TextControllers.StdCtrlDescTextControllers.ControllerDescContainers.ControllerDescControllers.ControllerDesc aY?$ ZGo * ,[ @Documents.ControllerDesc t]s ' `h*