WINBugs:数组索引大于数组上限

Question

我需要帮助才能在我的 WINBUGS 代码 (v. 1.4.3) 中找到错误。

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，出现此错误:

数组索引大于 phi3 的数组上限

有人可以帮助我吗？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)
) 

Answer 1

在 logit(eta[w]) 的定义中，您使用了 phi3[p[w]]，而 p[w] 的值从 9 到 14。但 phi3[j] 的定义仅针对 j = 1 到 JJ =8。因此“数组索引(9 到 14)大于数组上限(8)”