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 = 5或NT = length(unique(s))。
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 = 5或NT = length(unique(s))。