WinBUGS inits: "number of initial values not equal to length of vector"

Question

I can't find the problem with my BUGS model. The model syntax is correct, the data is loading fine, but I am having the following error message while loading my inits:

number of initial values not equal to length of vector

My code is provided below for your appreciation.

All the best!

• Camila

  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]]
  }
  
  mu1~ dnorm(0,1000)
  mu2~ dnorm(0,1000)
  mu3~ dnorm(0,1000)
  
  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
  }
  
  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])
  }
  
  
  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)
  
  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
  }
  
  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])
  }
  
  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)
  
  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
  }
  
  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])
  }
  
  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)
  
      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)
  }
  
  for (i in 1:I) {
      for (j in 1:JJ) {
          deltapred[i,j]   <- exp(mu1+theta1[i]+phi1[j]+psi1[I+j-i])
          thetapred[i,j]   <- exp(mu2+theta2[i]+phi2[j]+psi2[I+j-i])
          etapred[i,j]   <- exp(mu3+theta3[i]+phi3[j]+psi3[I+j-i])
          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(1,2,3,4,5,6,1,2,3,4,5,6,1,2,3,4,5,6,1,2,3,4,5,6,1,2,3,4,5,6,1,2,3,4,5,6,1,2,3,4,5,6,1,2,3,4,5,6),
  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,
  mu1=0,
  mu2=0,
  mu3=0,
  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

When the error message appears, the cursor should be placed after the erroneous vector in the initial values list. In your case this is phi1, because you have supplied 6 initial values, but in the model code, phi1[j] is defined for j=1 to JJ=8.