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R - 多个嵌套循环

[英]R - multiple nested loops

I am trying to write a nested loop code to simulate 10 columns of data in a data frame with 101 rows. 我试图编写一个嵌套循环代码来模拟101行数据框中的10列数据。 The first row of data has been assigned as starting values. 第一行数据已被指定为起始值。 Each column should be different as my matrix r is generated from random normals; 每列应该是不同的,因为我的矩阵r是从随机法线生成的; however, the resulting values in each column are exactly the same. 但是,每列中的结果值完全相同。 To give some context for the looping indices: 为循环索引提供一些上下文:

tmax=100; ncol(pop_sims) = 12 (so a total of 10 iterations, 3-12); ncol(r) = 10

for (i in 1:tmax){
 for (j in 3:ncol(pop_sims)){
   for(k in 1:ncol(r)){

   if (pop_sims[i,j]*exp(r[i,k]) <2) {
    pop_sims[i+1,j]<- 0} 
  else {
      pop_sims[i+1,j] <- pop_sims[i,j]*exp(r[i,k])}
}}} 

Any thoughts would be appreciated. 任何想法将不胜感激。

UPDATE: Instead of working with the multiple loops, I omitted the use of the matrix r and simplified my loops. 更新:我没有使用多个循环,而是省略了矩阵r的使用并简化了我的循环。

for (i in 1:tmax){
 for (j in 1:10){

  if (pop_sims[i,j]*exp(r[i,j]) <2) {
    pop_sims[i+1,j]<- 0} 
  else {
      pop_sims[i+1,j] <- pop_sims[i,j]*exp(rnorm(1,mean=0.02, sd=0.1))}
}}

Looping is almost never the best way to accomplish a task in R. It sounds like you want to multiply some initial value by the cumulative product of random numbers, and store this as the columns of a matrix. 循环几乎不是在R中完成任务的最佳方法。听起来你想要将一些初始值乘以随机数的累积乘积,并将其存储为矩阵的列。 cumprod is how to get a cumulative product in R: cumprod是如何获得R中的累积产品:

tmax = 100
initial.row = 1:10
rbind(initial.row, sapply(initial.row,
                          function(x) x*cumprod(exp(rnorm(tmax-1, mean=.02, sd=.1)))))

#                 [,1]       [,2]      [,3]     [,4]      [,5]      [,6]       [,7]       [,8]      [,9]     [,10]
# initial.row 1.000000   2.000000  3.000000 4.000000  5.000000  6.000000   7.000000   8.000000  9.000000 10.000000
#             1.211479   2.483438  3.376012 3.684407  5.611144  5.689530   6.115748   7.809380  8.364223 10.748108
#             1.221254   2.832064  3.500622 3.417095  5.047067  5.706181   6.445985   9.031854  8.125584 10.115107
#             1.350398   3.002284  3.723416 3.581471  5.121880  7.300373   8.008373  10.679728  7.167270 10.310810
#             1.269193   2.883153  3.546245 3.160029  5.312947  7.983395   6.986608  11.226517  7.166026  9.195459
#             ...

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