catch 值如何接近另一个向量 R 中的值

您可以使用outer创建一个距离矩阵，然后用tol度作为子集.为了找到最佳值，我们optimize在低容差和结果长度之间进行权衡，时间间隔在0和1之间.

f <- \(a) {
  r <- z[rowSums(abs(abs(outer(z, y, `-`))) < abs(y*a)) > 0]
  a + length(r)/length(z)
}
(tol <- optimize(f, c(0, 1))$objective)
# [1] 0.00983826

z[rowSums(abs(abs(outer(z, y, `-`))) < abs(y*tol)) > 0]
#   [1] 17.5 38.0 19.5 37.5 25.0 22.5  3.0 18.5 37.0 18.5 22.0 48.0 28.0 15.0
#  [15] 29.5  7.5 35.0 21.0  8.5 13.0 15.0 18.0 17.5 35.0 29.5 34.5 18.5 24.0
#  [29] 24.0 30.5 15.5 22.0 45.0 12.0 34.5 13.0 22.5 30.5 22.0 21.0  7.5 20.0
#  [43] 64.0 52.0  0.0 25.0 17.0 30.5 21.5 18.0 19.5 38.0 38.0 20.5  8.0 45.0
#  [57] 35.0 10.0 12.0 13.0  3.0 13.0 25.0  0.0 29.0 28.0  7.5 19.5 28.0 54.0
#  [71] 12.5  8.0 35.5 18.0 15.0 21.5  9.5 17.5 18.0 28.5 35.0 50.5 13.0 18.5
#  [85] 24.5 34.5  9.0 15.0 18.0 18.0 27.5 22.5 24.5 12.0 30.5  2.5 34.5  9.5
#  [99] 18.0 17.5 15.0 54.5 48.5  9.5 18.0  8.0 28.0 37.0 23.5 45.0 15.0 35.0
# [113] 20.5 17.0 22.5 25.0  8.0 12.5 24.0 34.0 12.0 28.0 18.5 20.0 20.0 17.0
# [127]  8.0 22.0 30.0 19.5  7.5  0.0 13.0 19.0  0.0 17.5

Data:个

y <- c(54.36, 70.43, 36.49, 39.59, 15.06, 34.6, 0.24, 24, 20.22, 24.73, 
29.86, 49.3, 37.36, 29.68, 37.64, 7.99, 22.42, 28.99, 3.09, 63.92, 
17.72, 9.51, 13.06, 3.83, 51.91, 1.79, 16.86, 50.74, 28.41, 20.03, 
24.24, 33.7, 7.7, 24.83, 53.98, -8.74, 30.14, 20.8, 12.1, 38.31, 
35.35, 33.96, 25.07, 44.86, 18.73, 36.66, 19.57, 30.62, -0.65, 
21.66, 54.59, 35.28, 27.83, 48.27, 17.99)

z <- c(53, 17.5, 48.5, 40, 46, 38, 14.5, 19.5, 37.5, 5.5, 16, 25, 
11, 17.5, 14, 21, 22.5, 0, 3, 18.5, 37, 53, 33, 47, 18.5, 22, 
14.5, 48.5, 48, 26, 28, 56.5, 15, 29.5, 7.5, 35, 7, 25.5, 21, 
8.5, 53, 51.5, 9, 15, 20.5, 13, 23, 15, 18, 38.5, 7, 17.5, 35, 
29.5, 16.5, 34.5, 27.5, 18.5, 18.5, 24, 24, 30.5, 28, 27, 15.5, 
12.5, 23, 22, 45, 12, 19, 10, 85.5, 16, 34.5, 43, 27, 13, 22.5, 
30.5, 22, 59, 21, 7.5, 23, 10.5, 16, 20, 62, 13, 64, 52, 18.5, 
33, 0, 25, 11, 16.5, 17, 48, 30.5, 21.5, 18, 19.5, 11, 95, 38, 
17.5, 42.5, 7, 48, 38, 23.5, 16.5, 7.5, 51, 14.5, 20.5, 23.5, 
8, 46, 45, 64, 75, 35, 10, 10, 10.5, 12, 12, 13, 15.5, 39, 29.5, 
3, 13, 25, 5, 0, 29, 28, 7.5, 14, 26.5, 19.5, 62, 23, 8.5, 31.5, 
23.5, 26, 11, 18.5, 28, 31, 42, 57, 54, 10, 12.5, 13.5, 11, 8, 
35.5, 60, 18, 101.5, 15, 21.5, 9.5, 17.5, 18, 16, 28.5, 35, 47, 
26, 50.5, 13, 18.5, 14, 18.5, 27, 33, 28.5, 24.5, 34.5, 9, 9, 
53.5, 15, 14, 18, 16.5, 27, 11.5, 14, 27, 10, 46, 0, 18, 27.5, 
67.5, 22.5, 12.5, 26, 24.5, 0, 22, 12, 30.5, 23.5, 2.5, 15.5, 
34.5, 50, 62, 5, 9.5, 11, 10.5, 6.5, 23, 12.5, 18, 17.5, 31.5, 
42.5, 15, 54.5, 48.5, 9.5, 16.5, 18, 42, 49, 4, 42, 47, 13.5, 
28, 8, 43, 8.5, 10, 26.5, 13.5, 28, 37, 23.5, 10.5, 45, 15, 10, 
35, 36, 20.5, 17, 22.5, 64.5, 21, 25, 8, 12.5, 21.5, 24, 8.5, 
27, 70, 34, 31.5, 12, 5.5, 36, 28, 6.5, 14.5, 18.5, 21.5, 20, 
23.5, 20, 27, 19, 17, 10, 50, 36, 8, 22, 58.5, 30, 19.5, 0, 22, 
0, 7.5, 0, 59, 13, 4.5, 19, 14, 0, 17.5, 19.5)