数组索引大于 d 的数组上限

array index is greater than array upper bound for d

谁能帮我解决这个问题?我使用了具有不同数量的研究 (NS) 和治疗 (NT) 的其他数据集,并且效果很好。
任何帮助将不胜感激。

数据集如下:

list(N=186, NS=5, NT=3, mean=c(0,0); 

哪里

N= number of intervals
NS= number of studies
NT= number of treatment 

s[]= Study ID       
r[]= no of events              
n[]= no at risk    
t[]= study arm ID                  
b[]= Study arm base        
time[]= time in months  
dt[]= difference in interval (months) 
model {
for (i in 1:N) {                                                                                # N=number of datapoints in dataset

#likelihood
r[i] ~dbin(p[i],n[i])
p[i]<-1- exp(-h[i]*dt[i])                                                   # hazard h over interval [t,t+dt] expressed as                                                                                                                  deaths per unit person-time (e.g. months)
#fixed effects model
log(h[i]) <- nu[i]+log(time[i])*theta[i]
nu[i]<-mu[s[i],1]+d[s[i],1]*(1-equals(t[i],b[i]))
theta[i]<-mu[s[i],2]+ d[s[i],2]*(1-equals(t[i],b[i]))
}

# priors
d[1,1]<- 0
d[1,2]<- 0
for(j in 2 :NT){                                                                # NT=number of treatments
    d[j,1:2] ~ dmnorm(mean[1:2],prec2[,])
}
for(k in 1:NS) {
mu[k,1:2] ~ dmnorm(mean[1:2],prec2[,])
}
}

#Winbugs data set

list(N=176, NS=5, NT=3, mean=c(0,0),
prec2 = structure(.Data = c(0.0001,0,0,0.0001), .Dim = c(2,2)))


# initials 1
list(
d=structure(.Data=c(NA,NA,0,0,0,0,0,0), .Dim = c(4,2)),
mu = structure(.Data=c(1,1,1,1,1,1,1,1), .Dim = c(4,2)))


# initials 2
list(
d=structure(.Data=c(NA,NA,0.5,0.5,0.5,0.5,0.5,0.5), .Dim = c(4,2)),
mu = structure(.Data=c(0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5), .Dim = c(4,2)))



s[] r[] n[] t[] b[] time[]  dt[]
1   1   62  1   1   3   2
1   2   59  1   1   7   4
1   6   53  1   1   11  2
1   2   51  1   1   13  2
1   3   48  1   1   15  2
1   2   45  1   1   17  2
1   5   40  1   1   19  2
1   2   37  1   1   23  4
1   2   35  1   1   25  2
1   2   32  1   1   27  2
1   1   31  1   1   29  2
1   2   28  1   1   31  2
1   2   26  1   1   33  2
1   2   23  1   1   35  2
1   1   21  1   1   39  4
1   1   14  1   1   51  12
1   2   55  2   1   5   4
1   1   54  2   1   7   2
1   2   52  2   1   9   2
1   1   51  2   1   11  2
1   5   46  2   1   13  2
1   2   44  2   1   15  2
1   3   41  2   1   17  2
1   3   37  2   1   19  2
1   2   35  2   1   21  2
1   1   34  2   1   23  2
1   1   33  2   1   25  2
1   1   32  2   1   31  6
1   3   29  2   1   33  2
1   1   28  2   1   35  2
1   1   26  2   1   39  4
1   1   24  2   1   41  2
1   1   22  2   1   43  2
1   2   19  2   1   45  2
2   8   169 1   1   3   4
2   10  148 1   1   5   2
2   8   137 1   1   7   2
2   6   127 1   1   9   2
2   8   118 1   1   11  2
2   7   109 1   1   13  2
2   3   105 1   1   15  2
2   4   95  1   1   17  2
2   3   84  1   1   19  2
2   3   76  1   1   21  2
2   4   68  1   1   23  2
2   4   60  1   1   25  2
2   4   50  1   1   27  2
2   1   35  1   1   31  4
2   2   29  1   1   33  2
2   1   25  1   1   35  2
2   3   21  1   1   37  2
2   1   18  1   1   39  2
2   2   11  1   1   43  4
2   1   180 2   1   1   2
2   11  162 2   1   3   2
2   9   147 2   1   5   2
2   9   135 2   1   7   2
2   6   125 2   1   9   2
2   6   116 2   1   11  2
2   6   106 2   1   13  2
2   7   95  2   1   15  2
2   1   92  2   1   17  2
2   5   84  2   1   19  2
2   3   77  2   1   21  2
2   2   67  2   1   23  2
2   1   59  2   1   25  2
2   4   49  2   1   27  2
2   1   40  2   1   29  2
2   2   34  2   1   31  2
2   3   23  2   1   37  6
2   1   19  2   1   39  2
4   1   62  1   1   3   2
4   2   59  1   1   7   4
4   6   53  1   1   11  2
4   2   51  1   1   13  2
4   3   48  1   1   15  2
4   2   45  1   1   17  2
4   5   40  1   1   19  2
4   2   37  1   1   23  4
4   2   35  1   1   25  2
4   2   32  1   1   27  2
4   1   31  1   1   29  2
4   2   28  1   1   31  2
4   2   26  1   1   33  2
4   2   23  1   1   35  2
4   1   21  1   1   39  4
4   1   14  1   1   51  12
4   2   55  2   1   5   4
4   1   54  2   1   7   2
4   2   52  2   1   9   2
4   1   51  2   1   11  2
4   5   46  2   1   13  2
4   2   44  2   1   15  2
4   3   41  2   1   17  2
4   3   37  2   1   19  2
4   2   35  2   1   21  2
4   1   34  2   1   23  2
4   1   33  2   1   25  2
4   1   32  2   1   31  6
4   3   29  2   1   33  2
4   1   28  2   1   35  2
4   1   26  2   1   39  4
4   1   24  2   1   41  2
4   1   22  2   1   43  2
4   2   19  2   1   45  2
5   8   169 1   1   3   4
5   10  148 1   1   5   2
5   8   137 1   1   7   2
5   6   127 1   1   9   2
5   8   118 1   1   11  2
5   7   109 1   1   13  2
5   3   105 1   1   15  2
5   4   95  1   1   17  2
5   3   84  1   1   19  2
5   3   76  1   1   21  2
5   4   68  1   1   23  2
5   4   60  1   1   25  2
5   4   50  1   1   27  2
5   1   35  1   1   31  4
5   2   29  1   1   33  2
5   1   25  1   1   35  2
5   3   21  1   1   37  2
5   1   18  1   1   39  2
5   2   11  1   1   43  4
5   1   180 2   1   1   2
5   11  162 2   1   3   2
5   9   147 2   1   5   2
5   9   135 2   1   7   2
5   6   125 2   1   9   2
5   6   116 2   1   11  2
5   6   106 2   1   13  2
5   7   95  2   1   15  2
5   1   92  2   1   17  2
5   5   84  2   1   19  2
5   3   77  2   1   21  2
5   2   67  2   1   23  2
5   1   59  2   1   25  2
5   4   49  2   1   27  2
5   1   40  2   1   29  2
5   2   34  2   1   31  2
5   3   23  2   1   37  6
5   1   19  2   1   39  2
3   2   179 1   1   1   2
3   4   172 1   1   3   2
3   3   168 1   1   5   2
3   6   157 1   1   7   2
3   4   151 1   1   9   2
3   9   142 1   1   11  2
3   10  130 1   1   13  2
3   7   123 1   1   15  2
3   3   119 1   1   17  2
3   5   112 1   1   19  2
3   3   108 1   1   21  2
3   3   103 1   1   23  2
3   12  91  1   1   25  2
3   2   68  1   1   27  2
3   2   46  1   1   29  2
3   8   29  1   1   31  2
3   2   23  1   1   33  2
3   3   8   1   1   35  2
3   5   175 3   1   3   4
3   7   163 3   1   5   2
3   12  151 3   1   7   2
3   12  139 3   1   9   2
3   4   132 3   1   11  2
3   9   122 3   1   13  2
3   7   114 3   1   15  2
3   4   108 3   1   17  2
3   7   101 3   1   19  2
3   5   96  3   1   21  2
3   7   89  3   1   23  2
3   2   87  3   1   25  2
3   4   68  3   1   27  2
3   4   50  3   1   29  2
3   3   40  3   1   31  2
3   3   22  3   1   33  2
3   1   8   3   1   35  2

END

您已设置 NT = 3,而索引 s 向量的范围为 1 到 5。

设置NT = 5NT = length(unique(s))