循环R矢量化的有效方法

如何将以下R代码矢量化以减少计算时间

q = matrix(0,n,p)
for(u in 1 : n){
    q1 <- matrix(0,p,1)
  for(iprime in 1 : n){
    for(i in 1 : n){
      if(cause[iprime]==1 & cause[i]>1 & (time[i]<time[u]) & (time[u] <= time[iprime])){
          q1 = q1 + (covs[i,] - S1byS0hat[iprime,])*G[iprime]/G[i]*expz[i]/S0hat[iprime]
      }
    }

  }
    q[u,] = q1/(m*m)
}

---

对您的解决方案进行基准测试：

coeff <- 10
n = 20 * coeff
m = 500
p = 3
G = runif(n)
time = runif(n, 0.01, 5)
cause = c(rep(0, 6 * coeff), rep(1, 10 * coeff), rep(2, 4 * coeff))
covs = matrix(rnorm(n * p), n, p)
S1byS0hat = matrix(rnorm(n * p), n, p)
S0hat = rnorm(n)
expz = rnorm(n)

system.time({
  q = matrix(0,n,p)
  for(u in 1 : n){
    q1 <- matrix(0,p,1)
    for(iprime in 1 : n){
      for(i in 1 : n){
        if(cause[iprime]==1 & cause[i]>1 & (time[i]<time[u]) & (time[u] <= time[iprime])){
          q1 = q1 + (covs[i,] - S1byS0hat[iprime,])*G[iprime]/G[i]*expz[i]/S0hat[iprime]
        }
      }

    }
    q[u,] = q1/(m*m)
  }
})

对于系数=10，这需要0.3秒，对于系数=100，这需要6分钟

---

然后，可以对至少一个循环进行矢量化：

q3 <- matrix(0, n, p)
c1 <- G / S0hat
c2 <- expz / G
covs_c2 <- sweep(covs, 1, c2, '*')
S1byS0hat_c1 <- sweep(S1byS0hat, 1, c1, '*')
for (u in 1:n) {
  q1 <- rep(0, p)
  ind_iprime <- which(cause == 1 & time[u] <= time)
  ind_i <- which(cause > 1 & time < time[u])
  for (iprime in ind_iprime) {
    q1 <- q1 + colSums(covs_c2[ind_i, , drop = FALSE]) * c1[iprime] - 
      S1byS0hat_c1[iprime, ] * sum(c2[ind_i])
  }
  q3[u, ] <- q1
}
q3 <- q3 / (m * m)

---

q3@F.Prive非常好，谢谢。我确实在Rcpp中编写了代码。这是@F.Prive链接，我用上面的代码和我的代码（在Rcpp中）比较了计算时间。对于coeff=100，您的代码在我的机器上花费了大约11秒，而我的代码在我的机器上花费了大约16秒。如果您对这个答案感到满意，您可以接受它。明天我可以从另一个问题快速查看Rcpp代码。当然可以。如果我们能进一步改进，那就太好了
q2 = matrix(0, n, p)
c1 <- G / S0hat
c2 <- expz / G
for (u in 1:n) {
  q1 <- rep(0, p)
  ind_iprime <- which(cause == 1 & time[u] <= time)
  ind_i <- which(cause > 1 & time < time[u])
  for (iprime in ind_iprime) {
    for (i in ind_i) {
      q1 = q1 + (covs[i, ] - S1byS0hat[iprime, ]) * c1[iprime] * c2[i]
    }
  }
  q2[u, ] = q1
}
q2 <- q2 / (m * m)

q3 <- matrix(0, n, p)
c1 <- G / S0hat
c2 <- expz / G
covs_c2 <- sweep(covs, 1, c2, '*')
S1byS0hat_c1 <- sweep(S1byS0hat, 1, c1, '*')
for (u in 1:n) {
  q1 <- rep(0, p)
  ind_iprime <- which(cause == 1 & time[u] <= time)
  ind_i <- which(cause > 1 & time < time[u])
  for (iprime in ind_iprime) {
    q1 <- q1 + colSums(covs_c2[ind_i, , drop = FALSE]) * c1[iprime] - 
      S1byS0hat_c1[iprime, ] * sum(c2[ind_i])
  }
  q3[u, ] <- q1
}
q3 <- q3 / (m * m)