符合此标准的观察结果。但是它给了我一个警告和错误的结果。试试这样：data%%>%mutate（myfun=q*pnorm（（p-b）/w））%%>%groupby（id，t，h，d）%%>%summary（sum（myfun））谢谢@Dave2e。对你有

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符合此标准的观察结果。但是它给了我一个警告和错误的结果。试试这样：

data%%>%mutate（myfun=q*pnorm（（p-b）/w））%%>%groupby（id，t，h，d）%%>%summary（sum（myfun））

谢谢@Dave2e。对你有用吗？它产生的输出仍然是一个不同数字的向量，而相同的

t

、

id

、

h

和

d

应该具有相同的值。

set.seed(1)
data <- data.frame(id = rep(c(rep(1,5), rep(2,5)),2), h = rep(1:2,10), 
                   d = c(rep(1,10), rep(2,10)), t = rep(c(sample(c(1,2,3), 5, replace = T),
                   sample(c(1,2,3), 5, replace = T)),2), q = runif(20), p = runif(20),
                   b = runif(20), w = rep(c(rep(.1,2), rep(.2,2)),5))

     id h d t          q         p          b   w          Sum       result
1.1   1 1 1 1 0.20597457 0.4820801 0.47761962 0.1 1.066513e-01 8.764928e-01
2.2   1 2 1 2 0.17655675 0.5995658 0.86120948 0.1 7.843788e-04 7.843788e-04
2.3   1 1 1 2 0.68702285 0.4935413 0.43809711 0.2 4.185307e-01 4.185307e-01
3.4   1 2 1 3 0.38410372 0.1862176 0.24479728 0.2 1.478031e-01 1.478031e-01
1.5   1 1 1 1 0.76984142 0.8273733 0.07067905 0.1 7.698414e-01 8.764928e-01
3.6   2 2 1 3 0.49769924 0.6684667 0.09946616 0.1 4.976992e-01 4.976992e-01
3.7   2 1 1 3 0.71761851 0.7942399 0.31627171 0.2 7.115705e-01 7.115705e-01
2.8   2 2 1 2 0.99190609 0.1079436 0.51863426 0.2 1.985233e-02 1.985233e-02
2.9   2 1 1 2 0.38003518 0.7237109 0.66200508 0.1 2.779585e-01 2.779585e-01
1.10  2 2 1 1 0.77744522 0.4112744 0.40683019 0.1 4.025021e-01 4.025021e-01
1.11  1 1 2 1 0.93470523 0.8209463 0.91287592 0.2 3.018017e-01 3.745763e-01
2.12  1 2 2 2 0.21214252 0.6470602 0.29360337 0.2 2.039559e-01 2.039559e-01
2.13  1 1 2 2 0.65167377 0.7829328 0.45906573 0.1 6.512825e-01 6.512825e-01
3.14  1 2 2 3 0.12555510 0.5530363 0.33239467 0.1 1.238378e-01 1.238378e-01
1.15  1 1 2 1 0.26722067 0.5297196 0.65087047 0.2 7.277459e-02 3.745763e-01
3.16  2 2 2 3 0.38611409 0.7893562 0.25801678 0.2 3.845907e-01 3.845907e-01
3.17  2 1 2 3 0.01339033 0.0233312 0.47854525 0.1 3.555325e-08 3.555325e-08
2.18  2 2 2 2 0.38238796 0.4772301 0.76631067 0.1 7.346728e-04 7.346728e-04
2.19  2 1 2 2 0.86969085 0.7323137 0.08424691 0.2 8.691717e-01 8.691717e-01
1.20  2 2 2 1 0.34034900 0.6927316 0.87532133 0.2 6.147884e-02 6.147884e-02

data$Aggregated.Sum <- ave(data[, c("q", "p", "b", "w")], 
                            data[,c("id", "h", "d", "t")], 
                            FUN=function(x){
                              sum(x$q * pnorm((x$p - x$b)/ x$w))
                            })[, 1]

   id h d t          q         p          b   w   Aggregated.Sum
1   1 1 1 1 0.20597457 0.4820801 0.47761962 0.1 0.87649276750008
2   1 2 1 2 0.17655675 0.5995658 0.86120948 0.1 0.00078437884830
3   1 1 1 2 0.68702285 0.4935413 0.43809711 0.2 0.41853074006211
4   1 2 1 3 0.38410372 0.1862176 0.24479728 0.2 0.14780307785185
5   1 1 1 1 0.76984142 0.8273733 0.07067905 0.1 0.87649276750008
6   2 2 1 3 0.49769924 0.6684667 0.09946616 0.1 0.49769923892396
7   2 1 1 3 0.71761851 0.7942399 0.31627171 0.2 0.71157053468214
8   2 2 1 2 0.99190609 0.1079436 0.51863426 0.2 0.01985232872822
9   2 1 1 2 0.38003518 0.7237109 0.66200508 0.1 0.27795848822967
10  2 2 1 1 0.77744522 0.4112744 0.40683019 0.1 0.40250214883360
11  1 1 2 1 0.93470523 0.8209463 0.91287592 0.2 0.37457632092225
12  1 2 2 2 0.21214252 0.6470602 0.29360337 0.2 0.20395587133630
13  1 1 2 2 0.65167377 0.7829328 0.45906573 0.1 0.65128247418102
14  1 2 2 3 0.12555510 0.5530363 0.33239467 0.1 0.12383782495223
15  1 1 2 1 0.26722067 0.5297196 0.65087047 0.2 0.37457632092225
16  2 2 2 3 0.38611409 0.7893562 0.25801678 0.2 0.38459067414776
17  2 1 2 3 0.01339033 0.0233312 0.47854525 0.1 0.00000003555325
18  2 2 2 2 0.38238796 0.4772301 0.76631067 0.1 0.00073467275547
19  2 1 2 2 0.86969085 0.7323137 0.08424691 0.2 0.86917168501551
20  2 2 2 1 0.34034900 0.6927316 0.87532133 0.2 0.06147884477362

data <- split(data, data$id)
data <- lapply(data, function(i) {split(i,i$t)})
data <- unlist(data,recursive=FALSE)

data1 <- lapply(data, function(j)
  {
  res <- j[,c("q","p","b","w")]
  j$result <- apply(res,1,function(i) i["q"] *pnorm( (i["p"] - i["b"]))/ i["w"] )
  j
  })

data1 <- do.call(rbind, data1)