我需要帮助才能在我的WINBUGS代码中找到错误（v.1.4.3）。
在“模型规范”步骤中，模型在语法上看起来是正确的。但是，在我尝试加载数据时，我收到了这个错误：
数组索引大于phi3的数组上限
有人可以帮帮我吗？我的模型如下：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+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(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)
)
答案 0 :(得分：5)
在logit（eta [w]）的定义中，您使用了phi3 [p [w]]，p [w]取值为9到14.但phi3 [j]的定义仅适用于j = 1到JJ = 8。因此“数组索引（9到14）大于数组上限（8）”