WINBugs: the array index is greater than the top of the array

Question

I need help to find the error in the WINBUGS code (version 1.4.3).

So far, the model looks correct in the step "Model specification". However, trying to load data, I got this error:

array index is greater than upper bound of array for phi3

Can someone please help me? My model is below:

model { for(w in 1: W){ m[w] <- n[w]-y1[w] h[w] <- n[w]-y1[w]-y2[w] y1[w] ~ dbin(delta[w],n[w]) y2[w] ~ dbin(theta[w],m[w]) y3[w] ~ dbin(eta[w],h[w]) y4[w] <- n[w]-y1[w]-y2[w]-y3[w] logit(delta[w]) <- mu1+theta1[a[w]]+phi1[p[w]]+psi1[c[w]] logit(theta[w]) <- mu2+theta2[a[w]]+phi2[p[w]]+psi2[c[w]] logit(eta[w]) <- mu3+theta3[a[w]]+phi3[p[w]]+psi3[c[w]] } ## Autoregressive prior model for p effects phi1mean[1] <- 0.0 phi1prec[1] <- tauphi1*1.0E-6 phi1mean[2] <- 0.0 phi1prec[2] <- tauphi1*1.0E-6 phi2mean[1] <- 0.0 phi2prec[1] <- tauphi2*1.0E-6 phi2mean[2] <- 0.0 phi2prec[2] <- tauphi2*1.0E-6 phi3mean[1] <- 0.0 phi3prec[1] <- tauphi3*1.0E-6 phi3mean[2] <- 0.0 phi3prec[2] <- tauphi3*1.0E-6 phi4mean[1] <- 0.0 phi4prec[1] <- tauphi4*1.0E-6 phi4mean[2] <- 0.0 phi4prec[2] <- tauphi4*1.0E-6 for (j in 3:JJ) { phi1mean[j] <- 2*phi1[j-1]-phi1[j-2] phi1prec[j] <- tauphi1 phi2mean[j] <- 2*phi2[j-1]-phi2[j-2] phi2prec[j] <- tauphi2 phi3mean[j] <- 2*phi3[j-1]-phi3[j-2] phi3prec[j] <- tauphi3 phi4mean[j] <- 2*phi4[j-1]-phi4[j-2] phi4prec[j] <- tauphi4 } # Sampling p effects and subtracting mean for observed p for (j in 1:JJ) { phi1[j] ~ dnorm(phi1mean[j],phi1prec[j]) phi2[j] ~ dnorm(phi2mean[j],phi2prec[j]) phi3[j] ~ dnorm(phi3mean[j],phi3prec[j]) phi4[j] ~ dnorm(phi4mean[j],phi4prec[j]) phi1c[j] <- phi1[j]-mean(phi1[1:J]) phi2c[j] <- phi2[j]-mean(phi2[1:J]) phi3c[j] <- phi3[j]-mean(phi3[1:J]) phi4c[j] <- phi4[j]-mean(phi4[1:J]) } # Hyppriors for the precision parameters tauphi1 ~ dgamma(1.0E-1,1.0E-1) tauphi2 ~ dgamma(1.0E-1,1.0E-1) tauphi3 ~ dgamma(1.0E-1,1.0E-1) tauphi4 ~ dgamma(1.0E-1,1.0E-1) sigmaphi1 <- 1/sqrt(tauphi1) sigmaphi2 <- 1/sqrt(tauphi2) sigmaphi3 <- 1/sqrt(tauphi3) sigmaphi4 <- 1/sqrt(tauphi4) ## Autoregressive prior model for c effects psi1mean[1] <- 0.0 psi1prec[1] <- taupsi1*1.0E-6 psi1mean[2] <- 0.0 psi1prec[2] <- taupsi1*1.0E-6 psi2mean[1] <- 0.0 psi2prec[1] <- taupsi2*1.0E-6 psi2mean[2] <- 0.0 psi2prec[2] <- taupsi2*1.0E-6 psi3mean[1] <- 0.0 psi3prec[1] <- taupsi3*1.0E-6 psi3mean[2] <- 0.0 psi3prec[2] <- taupsi3*1.0E-6 psi4mean[1] <- 0.0 psi4prec[1] <- taupsi4*1.0E-6 psi4mean[2] <- 0.0 psi4prec[2] <- taupsi4*1.0E-6 for (l in 3:LL) { psi1mean[l] <- 2*psi1[l-1]-psi1[l-2] psi1prec[l] <- taupsi1 psi2mean[l] <- 2*psi2[l-1]-psi2[l-2] psi2prec[l] <- taupsi2 psi3mean[l] <- 2*psi3[l-1]-psi3[l-2] psi3prec[l] <- taupsi3 psi4mean[l] <- 2*psi4[l-1]-psi4[l-2] psi4prec[l] <- taupsi4 } # Sampling c effects and subtracting mean for observed c for (l in 1:LL) { psi1[l] ~ dnorm(psi1mean[l],psi1prec[l]) psi2[l] ~ dnorm(psi2mean[l],psi2prec[l]) psi3[l] ~ dnorm(psi3mean[l],psi3prec[l]) psi4[l] ~ dnorm(psi4mean[l],psi4prec[l]) psi1c[l] <- psi1[l]-mean(psi1[1:L]) psi2c[l] <- psi2[l]-mean(psi2[1:L]) psi3c[l] <- psi3[l]-mean(psi3[1:L]) psi4c[l] <- psi4[l]-mean(psi4[1:L]) } # Hyppriors for the precision parameters taupsi1 ~ dgamma(1.0E-1,1.0E-1) taupsi2 ~ dgamma(1.0E-1,1.0E-1) taupsi3 ~ dgamma(1.0E-1,1.0E-1) taupsi4 ~ dgamma(1.0E-1,1.0E-1) sigmapsi1 <- 1/sqrt(taupsi1) sigmapsi2 <- 1/sqrt(taupsi2) sigmapsi3 <- 1/sqrt(taupsi3) sigmapsi4 <- 1/sqrt(taupsi4) ## Autoregressive prior model for a effects theta1mean[1] <- 0.0 theta1prec[1] <- tautheta1*1.0E-6 theta1mean[2] <- 0.0 theta1prec[2] <- tautheta1*1.0E-6 theta2mean[1] <- 0.0 theta2prec[1] <- tautheta2*1.0E-6 theta2mean[2] <- 0.0 theta2prec[2] <- tautheta2*1.0E-6 theta3mean[1] <- 0.0 theta3prec[1] <- tautheta3*1.0E-6 theta3mean[2] <- 0.0 theta3prec[2] <- tautheta3*1.0E-6 theta4mean[1] <- 0.0 theta4prec[1] <- tautheta4*1.0E-6 theta4mean[2] <- 0.0 theta4prec[2] <- tautheta4*1.0E-6 for (i in 3:I) { theta1mean[i] <- 2*theta1[i-1]-theta1[i-2] theta1prec[i] <- tautheta1 theta2mean[i] <- 2*theta2[i-1]-theta2[i-2] theta2prec[i] <- tautheta2 theta3mean[i] <- 2*theta3[i-1]-theta3[i-2] theta3prec[i] <- tautheta3 theta4mean[i] <- 2*theta4[i-1]-theta4[i-2] theta4prec[i] <- tautheta4 } # Sampling a effects for (i in 1:I) { theta1[i] ~ dnorm(theta1mean[i],theta1prec[i]) theta2[i] ~ dnorm(theta2mean[i],theta2prec[i]) theta3[i] ~ dnorm(theta3mean[i],theta3prec[i]) theta4[i] ~ dnorm(theta4mean[i],theta4prec[i]) } # Hyppriors for the precision parameters tautheta1 ~ dgamma(1.0E-1,1.0E-1) tautheta2 ~ dgamma(1.0E-1,1.0E-1) tautheta3 ~ dgamma(1.0E-1,1.0E-1) tautheta4 ~ dgamma(1.0E-1,1.0E-1) sigmatheta1 <- 1/sqrt(tautheta1) sigmatheta2 <- 1/sqrt(tautheta2) sigmatheta3 <- 1/sqrt(tautheta3) sigmatheta4 <- 1/sqrt(tautheta4) # Removing linear trends from a for (i in 1:I) { ivec1[i] <- i-(I+1)/2 aivec1[i] <- ivec1[i]*theta1[i] theta1c[i] <- theta1[i]-ivec1[i]*sum(aivec1[])/(I*(I+1)*(I-1)/12) ivec2[i] <- i-(I+1)/2 aivec2[i] <- ivec2[i]*theta2[i] theta2c[i] <- theta2[i]-ivec2[i]*sum(aivec2[])/(I*(I+1)*(I-1)/12) ivec3[i] <- i-(I+1)/2 aivec3[i] <- ivec3[i]*theta3[i] theta3c[i] <- theta3[i]-ivec3[i]*sum(aivec3[])/(I*(I+1)*(I-1)/12) ivec4[i] <- i-(I+1)/2 aivec4[i] <- ivec4[i]*theta4[i] theta4c[i] <- theta4[i]-ivec4[i]*sum(aivec4[])/(I*(I+1)*(I-1)/12) } ## Computing fitted and projected probabilities for (i in 1:I) { for (j in 1:JJ) { deltapred[i,j] <- exp(mu1+theta1[i]+phi1[j]+psi1[I+ji]) thetapred[i,j] <- exp(mu2+theta2[i]+phi2[j]+psi2[I+ji]) etapred[i,j] <- exp(mu3+theta3[i]+phi3[j]+psi3[I+ji]) p1[i,j] <- deltapred[i,j] p2[i,j] <- thetapred[i,j]*(1-deltapred[i,j]) p3[i,j] <- etapred[i,j]*(1-deltapred[i,j])*(1-thetapred[i,j]) p4[i,j] <- (1-deltapred[i,j])*(1-thetapred[i,j]-etapred[i,j]+(etapred[i,j]*thetapred[i,j])) } } } ### Data list( y1=c(1538727,1444672,1206999,1002960,744597,390301,1640130,1472255,1383947,1109395,984775,697701,1769569,1573498,1489025,1351284,1111397,935166,1747764,1790841,1626852,1407388,1284583,995236,1676555,1787181,1655400,1527122,1421772,1309989,1561922,1643467,1598855,1570645,1495999,1319439,1456258,1561892,1567872,1555237,1551579,1532222,1243436,1387943,1436659,1511134,1549578,1539580), y2=c(2634569,3031916,3138776,2875868,2495888,1886174,2148776,2567507,2747428,2696199,2593985,2138303,1662296,2224336,2489723,2698322,2655746,2450716,1304387,1734318,2180203,2396749,2629088,2555934,1087351,1380119,1616309,2109287,2408800,2369855,821642,1041702,1221283,1661647,2098345,2426842,708327,873092,952245,1237084,1628334,2123709,549763,666699,774205,981393,1243888,1538431), y3=c(1245931,1664176,1659375,2313647,3850196,4254634,825634,1293382,1454776,1736181,2596719,3655532,554953,901957,1186747,1490664,2083400,2738988,335824,630232,847486,1239538,1702256,2296941,218213,373786,555286,907876,1397221,2005940,143202,237344,344229,594993,1012777,1510283,121187,151070,219731,351040,650930,1157146,87211,120279,140551,226530,393887,733699), n=c(5862309,6673625,6534802,6942747,8329067,8152696,5049199,5913474,6268113,6253757,7298375,8260640,4319559,5245545,5840408,6306245,6785242,7492958,3588778,4553684,5259609,5813653,6517271,7001560,3105173,3797508,4271831,5180290,6086716,7002991,2591140,3063506,3428373,4305319,5326889,6217360,2329398,2661972,2886111,3418403,4327922,5565798,1906676,2224544,2444586,2864892,3473404,4362648), a=c(1,1,1,1,1,1,2,2,2,2,2,2,3,3,3,3,3,3,4,4,4,4,4,4,5,5,5,5,5,5,6,6,6,6,6,6,7,7,7,7,7,7,8,8,8,8,8,8), p=c(9,10,11,12,13,14,9,10,11,12,13,14,9,10,11,12,13,14,9,10,11,12,13,14,9,10,11,12,13,14,9,10,11,12,13,14,9,10,11,12,13,14,9,10,11,12,13,14), c=c(8,9,10,11,12,13,7,8,9,10,11,12,6,7,8,9,10,11,5,6,7,8,9,10,4,5,6,7,8,9,3,4,5,6,7,8,2,3,4,5,6,7,1,2,3,4,5,6), W=48, I=8, J=6, JJ=8, L=13, LL=15 ) # Inits list( tauphi1=1, tauphi2=1, tauphi3=1, tauphi4=1, taupsi1=1, taupsi2=1, taupsi3=1, taupsi4=1, tautheta1=1, tautheta2=1, tautheta3=1, tautheta4=1, theta1=c(0,0,0,0,0,0,0,0), theta2=c(0,0,0,0,0,0,0,0), theta3=c(0,0,0,0,0,0,0,0), theta4=c(0,0,0,0,0,0,0,0), phi1=c(0,0,0,0,0,0), phi2=c(0,0,0,0,0,0), phi3=c(0,0,0,0,0,0), phi4=c(0,0,0,0,0,0), psi1=c(0,0,0,0,0,0,0,0,0,0,0,0,0), psi2=c(0,0,0,0,0,0,0,0,0,0,0,0,0), psi3=c(0,0,0,0,0,0,0,0,0,0,0,0,0), psi4=c(0,0,0,0,0,0,0,0,0,0,0,0,0) ) 

Answer 1

In the definition of logit (eta [w]), you used phi3 [p [w]], and p [w] takes values ​​from 9 to 14. But the definitions of phi3 [j] are given only for j = 1 to JJ = 8. Therefore, "the array index (9 to 14) is greater than the upper bound of the array (8)"