R 以增长率投影时避免循环

我有一组模拟增长率，比如说8个时间段（和5条模拟增长路径）

---

是否可以加速/避免使用带有

apply

类型函数的

for

循环？

您的任务取决于先前的值。这似乎是递归函数的工作。但我不确定是否会加速。这是一个版本，它本身不使用递归函数，而是独立计算值（使用递归原则）：

---

my_fun您似乎需要的是一行
r+1
。对于我的“数据”

（不要问我为什么这里需要
t（）
）

然后，一切都是与初始大小相乘的问题：

> t(apply(r+1, 1, cumprod)) * 100
         [,1]     [,2]     [,3]     [,4]     [,5]     [,6]     [,7]     [,8]
[1,] 104.3056 108.0034 114.3569 118.8293 124.5284 131.5412 135.1246 139.8755
[2,] 105.6231 109.5816 114.8524 121.3355 129.5225 136.5245 142.3756 148.3844
[3,] 104.3894 109.9783 115.0379 120.0575 126.5403 131.9313 137.6076 146.7751
[4,] 104.6446 110.3700 117.3011 119.8769 127.0356 133.4186 140.0210 145.9804
[5,] 104.4265 107.8663 112.5322 119.2916 128.1390 136.5771 142.9413 147.7954

哦，别忘了为初始大小添加一列：

> cbind(1, t(apply(r+1, 1, cumprod))) * 100
     [,1]     [,2]     [,3]     [,4]     [,5]     [,6]     [,7]     [,8]
[1,]  100 104.3056 108.0034 114.3569 118.8293 124.5284 131.5412 135.1246
[2,]  100 105.6231 109.5816 114.8524 121.3355 129.5225 136.5245 142.3756
[3,]  100 104.3894 109.9783 115.0379 120.0575 126.5403 131.9313 137.6076
[4,]  100 104.6446 110.3700 117.3011 119.8769 127.0356 133.4186 140.0210
[5,]  100 104.4265 107.8663 112.5322 119.2916 128.1390 136.5771 142.9413
         [,9]
[1,] 139.8755
[2,] 148.3844
[3,] 146.7751
[4,] 145.9804
[5,] 147.7954

您可能对阅读Patrick Burns的《R地狱》感兴趣。我发现在这种情况下获得主要加速的最佳方法是使用
Rcpp
软件包。非常快！
my_fun <- function(x, rr, idx) {
    i <- 1
    xx <- rep(x, nrow(rr))
    while( i <= idx) {
        xx <- xx * (1 + rr[, i])
        i <- i + 1
    }
    xx
}
apply(as.matrix(0:ncol(r), ncol=1), 1, function(ix) my_fun(100, r, ix))

> r <- matrix(rnorm(40,0.05,0.01),5,8)
> r
           [,1]       [,2]       [,3]       [,4]       [,5]       [,6]
[1,] 0.04305611 0.03545166 0.05882694 0.03910892 0.04796011 0.05631498
[2,] 0.05623084 0.03747785 0.04809927 0.05644677 0.06747468 0.05405979
[3,] 0.04389437 0.05353846 0.04600529 0.04363427 0.05399780 0.04260270
[4,] 0.04644610 0.05471288 0.06279882 0.02195831 0.05971777 0.05024525
[5,] 0.04426485 0.03294009 0.04325665 0.06006569 0.07416615 0.06585176
           [,7]       [,8]
[1,] 0.02724187 0.03515898
[2,] 0.04285792 0.04220358
[3,] 0.04302487 0.06662048
[4,] 0.04948629 0.04256084
[5,] 0.04659738 0.03395883

> t(apply(r+1, 1, cumprod))
         [,1]     [,2]     [,3]     [,4]     [,5]     [,6]     [,7]     [,8]
[1,] 1.043056 1.080034 1.143569 1.188293 1.245284 1.315412 1.351246 1.398755
[2,] 1.056231 1.095816 1.148524 1.213355 1.295225 1.365245 1.423756 1.483844
[3,] 1.043894 1.099783 1.150379 1.200575 1.265403 1.319313 1.376076 1.467751
[4,] 1.046446 1.103700 1.173011 1.198769 1.270356 1.334186 1.400210 1.459804
[5,] 1.044265 1.078663 1.125322 1.192916 1.281390 1.365771 1.429413 1.477954

> t(apply(r+1, 1, cumprod)) * 100
         [,1]     [,2]     [,3]     [,4]     [,5]     [,6]     [,7]     [,8]
[1,] 104.3056 108.0034 114.3569 118.8293 124.5284 131.5412 135.1246 139.8755
[2,] 105.6231 109.5816 114.8524 121.3355 129.5225 136.5245 142.3756 148.3844
[3,] 104.3894 109.9783 115.0379 120.0575 126.5403 131.9313 137.6076 146.7751
[4,] 104.6446 110.3700 117.3011 119.8769 127.0356 133.4186 140.0210 145.9804
[5,] 104.4265 107.8663 112.5322 119.2916 128.1390 136.5771 142.9413 147.7954

> cbind(1, t(apply(r+1, 1, cumprod))) * 100
     [,1]     [,2]     [,3]     [,4]     [,5]     [,6]     [,7]     [,8]
[1,]  100 104.3056 108.0034 114.3569 118.8293 124.5284 131.5412 135.1246
[2,]  100 105.6231 109.5816 114.8524 121.3355 129.5225 136.5245 142.3756
[3,]  100 104.3894 109.9783 115.0379 120.0575 126.5403 131.9313 137.6076
[4,]  100 104.6446 110.3700 117.3011 119.8769 127.0356 133.4186 140.0210
[5,]  100 104.4265 107.8663 112.5322 119.2916 128.1390 136.5771 142.9413
         [,9]
[1,] 139.8755
[2,] 148.3844
[3,] 146.7751
[4,] 145.9804
[5,] 147.7954