我正在使用R运行100,000个仿真，并且此代码块是仿真过程的一部分。我知道R对于for循环不是很友好，因此我想使用（我认为）一个apply函数来优化我的循环，尽管我还没有弄清楚。
我已经尝试了一个嵌套的sapply，但是它似乎只能用于满足if语句的第一行。
settlements <- matrix(data = NA, nrow = 40, ncol = 5)
settlements[-seq(3, 40, 3), ] <- 0
for(i in seq(3, 40, 3)){
for(j in 1:ncol(settlements)){
if(j > i){
settlements[i, j] <- 0
}else{
settlements[i, j] <- max(min(sum(expected_claims_pricing_cumulative[c(1:i), j]), sum(actual_claims[c(1:i), j])) - sum(expected_claims_discounted_cumulative[c(1:i), j]) - sum(settlements[c(1:(i - 1)), j]), 0)
}
}
}
因此上面的代码块可以工作，但是效率很低。
这是代码的示例输出：
[,1] [,2] [,3] [,4] [,5]
[1,] 0.0 0 0.0 0.0 0
[2,] 0.0 0 0.0 0.0 0
[3,] 121571.9 0 297009.7 0.0 0
[4,] 0.0 0 0.0 0.0 0
[5,] 0.0 0 0.0 0.0 0
[6,] 217259.3 0 881364.8 1543090.3 1821937
[7,] 0.0 0 0.0 0.0 0
[8,] 0.0 0 0.0 0.0 0
[9,] 219615.8 0 1398676.8 2050454.9 2788436
[10,] 0.0 0 0.0 0.0 0
[11,] 0.0 0 0.0 0.0 0
[12,] 577947.7 0 2007643.5 2995483.9 4163100
[13,] 0.0 0 0.0 0.0 0
[14,] 0.0 0 0.0 0.0 0
[15,] 641731.9 1184557 2148414.1 4253819.1 5908208
[16,] 0.0 0 0.0 0.0 0
[17,] 0.0 0 0.0 0.0 0
[18,] 862253.0 0 0.0 5727515.7 8081478
[19,] 0.0 0 0.0 0.0 0
[20,] 0.0 0 0.0 0.0 0
[21,] 796571.5 0 1588740.9 7260817.4 10380325
[22,] 0.0 0 0.0 0.0 0
[23,] 0.0 0 0.0 0.0 0
[24,] 302051.9 0 0.0 4496129.9 0
[25,] 0.0 0 0.0 0.0 0
[26,] 0.0 0 0.0 0.0 0
[27,] 112847.7 0 0.0 2148951.8 0
[28,] 0.0 0 0.0 0.0 0
[29,] 0.0 0 0.0 0.0 0
[30,] 0.0 0 0.0 338543.2 0
[31,] 0.0 0 0.0 0.0 0
[32,] 0.0 0 0.0 0.0 0
[33,] 0.0 0 0.0 0.0 0
[34,] 0.0 0 0.0 0.0 0
[35,] 0.0 0 0.0 0.0 0
[36,] 0.0 0 0.0 0.0 0
[37,] 0.0 0 0.0 0.0 0
[38,] 0.0 0 0.0 0.0 0
[39,] 0.0 0 0.0 0.0 0
[40,] 0.0 0 0.0 0.0 0
预计索赔价格累计：
1 246504.7 246504.7 246504.7 246504.7 246504.7
2 359684.6 729441.6 729441.6 729441.6 729441.6
3 450958.0 990484.9 1606746.6 1606746.6 1606746.6
4 536705.6 1213142.7 2112354.2 2851868.2 2851868.2
5 616642.8 1421701.3 2549096.4 3628150.1 4614168.9
6 735863.2 1660827.4 3002591.5 4355465.6 5794203.9
7 844157.6 1947952.3 3489559.4 5099676.3 6903508.4
8 958274.5 2224510.9 4064168.9 5914097.3 8060919.8
9 1060873.2 2498285.0 4608679.0 6816268.5 9282839.9
10 1177249.5 2768559.4 5164245.7 7696718.5 10640171.2
11 1358375.2 3124249.5 5776432.6 8651256.2 12027886.6
12 1536404.6 3573967.4 6517091.2 9699710.9 13532809.1
13 1712181.1 4016788.0 7412726.0 10944474.6 15187967.5
14 1868723.0 4436994.7 8278006.1 12353131.8 17062129.9
15 1999373.6 4802458.2 9082910.9 13692124.6 19125625.6
16 2289300.0 5288360.4 9960168.0 15096711.3 21242329.6
17 2492864.7 5926814.7 10925248.8 16531417.9 23380142.2
18 2715687.3 6454984.3 12178234.4 18176355.2 25651247.4
19 2955382.3 7028913.2 13261074.9 20128975.0 28126469.5
20 3200558.6 7633632.0 14422850.2 21901444.3 31058644.4
21 770767.5 5571605.4 12960061.0 21107123.0 31078581.7
22 823184.7 1979336.0 9980732.5 18846879.2 29709628.5
23 875275.7 2110052.8 4036971.6 13638647.3 25460176.4
24 928077.7 2240991.2 4298953.1 6611255.6 19413489.9
25 981284.6 2373401.1 4561590.4 7031144.6 10114214.6
26 0.0 1471926.9 3792121.1 6417948.2 9710687.1
27 0.0 0.0 2453211.5 5237444.5 8738547.3
28 0.0 0.0 0.0 2943853.8 6656164.5
29 0.0 0.0 0.0 0.0 3925138.4
30 0.0 0.0 0.0 0.0 0.0
31 0.0 0.0 0.0 0.0 0.0
32 0.0 0.0 0.0 0.0 0.0
33 0.0 0.0 0.0 0.0 0.0
34 0.0 0.0 0.0 0.0 0.0
35 0.0 0.0 0.0 0.0 0.0
36 0.0 0.0 0.0 0.0 0.0
37 0.0 0.0 0.0 0.0 0.0
38 0.0 0.0 0.0 0.0 0.0
39 0.0 0.0 0.0 0.0 0.0
40 0.0 0.0 0.0 0.0 0.0
预期的索赔已累计折现：
1 218156.6 218156.6 218156.6 218156.6 218156.6
2 318320.9 645555.8 645555.8 645555.8 645555.8
3 399097.9 876579.2 1421970.8 1421970.8 1421970.8
4 474984.5 1073631.3 1869433.4 2523903.4 2523903.4
5 545728.9 1258205.6 2255950.3 3210912.9 4083539.5
6 651238.9 1469832.3 2657293.5 3854587.0 5127870.5
7 747079.5 1723937.8 3088260.1 4513213.5 6109605.0
8 848073.0 1968692.2 3596789.4 5233976.2 7133914.1
9 938872.8 2210982.3 4078680.9 6032397.7 8215313.3
10 1041865.8 2450175.0 4570357.5 6811595.9 9416551.5
11 1202162.1 2764960.8 5112142.9 7656361.8 10644679.6
12 1359718.1 3162961.2 5767625.8 8584244.2 11976536.1
13 1515280.3 3554857.3 6560262.6 9685860.0 13441351.3
14 1653819.9 3926740.3 7326035.4 10932521.7 15099985.0
15 1769445.7 4250175.5 8038376.2 12117530.3 16926178.6
16 2026030.5 4680199.0 8814748.7 13360589.5 18799461.7
17 2206185.2 5245231.0 9668845.1 14630304.8 20691425.9
18 2403383.3 5712661.1 10777737.4 16086074.4 22701353.9
19 2615513.3 6220588.2 11736051.3 17814142.9 24891925.5
20 2832494.4 6755764.3 12764222.5 19382778.2 27486900.3
21 682129.2 4930870.8 11469654.0 18679803.8 27504544.8
22 728518.5 1751712.4 8832948.2 16679488.1 26293021.2
23 774619.0 1867396.7 3572719.8 12070202.9 22532256.1
24 821348.7 1983277.2 3804573.5 5850961.2 17180938.6
25 868436.9 2100460.0 4037007.5 6222562.9 8951079.9
26 0.0 1302655.3 3356027.1 5679884.1 8593958.1
27 0.0 0.0 2171092.2 4635138.4 7733614.4
28 0.0 0.0 0.0 2605310.6 5890705.5
29 0.0 0.0 0.0 0.0 3473747.5
30 0.0 0.0 0.0 0.0 0.0
31 0.0 0.0 0.0 0.0 0.0
32 0.0 0.0 0.0 0.0 0.0
33 0.0 0.0 0.0 0.0 0.0
34 0.0 0.0 0.0 0.0 0.0
35 0.0 0.0 0.0 0.0 0.0
36 0.0 0.0 0.0 0.0 0.0
37 0.0 0.0 0.0 0.0 0.0
38 0.0 0.0 0.0 0.0 0.0
39 0.0 0.0 0.0 0.0 0.0
40 0.0 0.0 0.0 0.0 0.0
实际声明：
[,1] [,2] [,3] [,4] [,5]
[1,] 0 0 0 60000 610000
[2,] 0 420000 530000 960000 410000
[3,] 1110000 660000 4140000 2260000 810000
[4,] 330000 750000 3550000 3480000 1070000
[5,] 1790000 850000 2140000 5090000 6120000
[6,] 2110000 540000 3940000 7440000 9500000
[7,] 0 1260000 2170000 7010000 9110000
[8,] 120000 3520000 5280000 6260000 10900000
[9,] 240000 210000 2610000 5730000 9140000
[10,] 2130000 2070000 5840000 14570000 6600000
[11,] 1430000 5110000 5810000 13540000 18910000
[12,] 860000 1140000 3970000 11630000 14430000
[13,] 2410000 5890000 8360000 13220000 15890000
[14,] 1550000 950000 9820000 12150000 21450000
[15,] 1960000 9370000 5780000 7740000 18530000
[16,] 4160000 5430000 4590000 14150000 17190000
[17,] 3930000 3450000 8190000 16840000 20080000
[18,] 590000 5290000 11690000 17580000 25380000
[19,] 2760000 4470000 19390000 20990000 28200000
[20,] 3450000 7140000 9740000 21740000 21070000
[21,] 820000 3780000 13220000 24690000 33260000
[22,] 0 2410000 4380000 19070000 30780000
[23,] 1930000 360000 2030000 9470000 15680000
[24,] 2460000 3620000 3140000 1540000 12560000
[25,] 0 4170000 6600000 5970000 8770000
[26,] 0 1280000 6890000 4940000 4530000
[27,] 0 0 740000 2880000 8280000
[28,] 0 0 0 4020000 9550000
[29,] 0 0 0 0 4960000
[30,] 0 0 0 0 0
[31,] 0 0 0 0 0
[32,] 0 0 0 0 0
[33,] 0 0 0 0 0
[34,] 0 0 0 0 0
[35,] 0 0 0 0 0
[36,] 0 0 0 0 0
[37,] 0 0 0 0 0
[38,] 0 0 0 0 0
[39,] 0 0 0 0 0
[40,] 0 0 0 0 0
答案 0 :(得分：3)
您可以使用outer来获取元素，并使用[<-或replace来放入元素。
settlements <- matrix(data = 0, nrow = 40, ncol = 5)
myfun <- function(i, j){
3*i/sqrt(j)
}
settlements[seq(3, nrow(settlements), 3),] <-
outer(seq(3, nrow(settlements), 3), seq(ncol(settlements)), myfun)
# or
# settlements[seq(3, nrow(settlements), 3),] <-
# do.call(myfun,
# expand.grid(i = seq(3, nrow(settlements), 3),
# j = seq(ncol(settlements))))
# or replace(settlements, row(settlements) %% 3 == 0, outer_output)
# if you want to create a new matrix
settlements
# [,1] [,2] [,3] [,4] [,5]
# [1,] 0 0.000000 0.000000 0.0 0.000000
# [2,] 0 0.000000 0.000000 0.0 0.000000
# [3,] 9 6.363961 5.196152 4.5 4.024922
# [4,] 0 0.000000 0.000000 0.0 0.000000
# [5,] 0 0.000000 0.000000 0.0 0.000000
# [6,] 18 12.727922 10.392305 9.0 8.049845
# [7,] 0 0.000000 0.000000 0.0 0.000000
# [8,] 0 0.000000 0.000000 0.0 0.000000
# [9,] 27 19.091883 15.588457 13.5 12.074767
# [10,] 0 0.000000 0.000000 0.0 0.000000
# [11,] 0 0.000000 0.000000 0.0 0.000000
# [12,] 36 25.455844 20.784610 18.0 16.099689
# [13,] 0 0.000000 0.000000 0.0 0.000000
# [14,] 0 0.000000 0.000000 0.0 0.000000
# [15,] 45 31.819805 25.980762 22.5 20.124612
# [16,] 0 0.000000 0.000000 0.0 0.000000
# [17,] 0 0.000000 0.000000 0.0 0.000000
# [18,] 54 38.183766 31.176915 27.0 24.149534
# [19,] 0 0.000000 0.000000 0.0 0.000000
# [20,] 0 0.000000 0.000000 0.0 0.000000
# [21,] 63 44.547727 36.373067 31.5 28.174457
# [22,] 0 0.000000 0.000000 0.0 0.000000
# [23,] 0 0.000000 0.000000 0.0 0.000000
# [24,] 72 50.911688 41.569219 36.0 32.199379
# [25,] 0 0.000000 0.000000 0.0 0.000000
# [26,] 0 0.000000 0.000000 0.0 0.000000
# [27,] 81 57.275649 46.765372 40.5 36.224301
# [28,] 0 0.000000 0.000000 0.0 0.000000
# [29,] 0 0.000000 0.000000 0.0 0.000000
# [30,] 90 63.639610 51.961524 45.0 40.249224
# [31,] 0 0.000000 0.000000 0.0 0.000000
# [32,] 0 0.000000 0.000000 0.0 0.000000
# [33,] 99 70.003571 57.157677 49.5 44.274146
# [34,] 0 0.000000 0.000000 0.0 0.000000
# [35,] 0 0.000000 0.000000 0.0 0.000000
# [36,] 108 76.367532 62.353829 54.0 48.299068
# [37,] 0 0.000000 0.000000 0.0 0.000000
# [38,] 0 0.000000 0.000000 0.0 0.000000
# [39,] 117 82.731493 67.549981 58.5 52.323991
# [40,] 0 0.000000 0.000000 0.0 0.000000
您始终可以直接根据row()和col()编写操作来创建新矩阵
row(settlements)/3 + col(settlements)^2.5
# [,1] [,2] [,3] [,4] [,5]
# [1,] 1.333333 5.990188 15.92179 32.33333 56.23503
# [2,] 1.666667 6.323521 16.25512 32.66667 56.56837
# [3,] 2.000000 6.656854 16.58846 33.00000 56.90170
# [4,] 2.333333 6.990188 16.92179 33.33333 57.23503
# [5,] 2.666667 7.323521 17.25512 33.66667 57.56837
# [6,] 3.000000 7.656854 17.58846 34.00000 57.90170
# [7,] 3.333333 7.990188 17.92179 34.33333 58.23503
# [8,] 3.666667 8.323521 18.25512 34.66667 58.56837
# [9,] 4.000000 8.656854 18.58846 35.00000 58.90170
# [10,] 4.333333 8.990188 18.92179 35.33333 59.23503
# [11,] 4.666667 9.323521 19.25512 35.66667 59.56837
# [12,] 5.000000 9.656854 19.58846 36.00000 59.90170
# [13,] 5.333333 9.990188 19.92179 36.33333 60.23503
# [14,] 5.666667 10.323521 20.25512 36.66667 60.56837
# [15,] 6.000000 10.656854 20.58846 37.00000 60.90170
# [16,] 6.333333 10.990188 20.92179 37.33333 61.23503
# [17,] 6.666667 11.323521 21.25512 37.66667 61.56837
# [18,] 7.000000 11.656854 21.58846 38.00000 61.90170
# [19,] 7.333333 11.990188 21.92179 38.33333 62.23503
# [20,] 7.666667 12.323521 22.25512 38.66667 62.56837
# [21,] 8.000000 12.656854 22.58846 39.00000 62.90170
# [22,] 8.333333 12.990188 22.92179 39.33333 63.23503
# [23,] 8.666667 13.323521 23.25512 39.66667 63.56837
# [24,] 9.000000 13.656854 23.58846 40.00000 63.90170
# [25,] 9.333333 13.990188 23.92179 40.33333 64.23503
# [26,] 9.666667 14.323521 24.25512 40.66667 64.56837
# [27,] 10.000000 14.656854 24.58846 41.00000 64.90170
# [28,] 10.333333 14.990188 24.92179 41.33333 65.23503
# [29,] 10.666667 15.323521 25.25512 41.66667 65.56837
# [30,] 11.000000 15.656854 25.58846 42.00000 65.90170
# [31,] 11.333333 15.990188 25.92179 42.33333 66.23503
# [32,] 11.666667 16.323521 26.25512 42.66667 66.56837
# [33,] 12.000000 16.656854 26.58846 43.00000 66.90170
# [34,] 12.333333 16.990188 26.92179 43.33333 67.23503
# [35,] 12.666667 17.323521 27.25512 43.66667 67.56837
# [36,] 13.000000 17.656854 27.58846 44.00000 67.90170
# [37,] 13.333333 17.990188 27.92179 44.33333 68.23503
# [38,] 13.666667 18.323521 28.25512 44.66667 68.56837
# [39,] 14.000000 18.656854 28.58846 45.00000 68.90170
# [40,] 14.333333 18.990188 28.92179 45.33333 69.23503
使用ifelse
new <-
ifelse(sqrt(row(settlements)*col(settlements)) > 5,
row(settlements)*col(settlements)^1.2,
row(settlements) + 44)
dim(new) <- dim(settlements)
new
# [,1] [,2] [,3] [,4] [,5]
# [1,] 45 45.00000 45.00000 45.00000 45.00000
# [2,] 46 46.00000 46.00000 46.00000 46.00000
# [3,] 47 47.00000 47.00000 47.00000 47.00000
# [4,] 48 48.00000 48.00000 48.00000 48.00000
# [5,] 49 49.00000 49.00000 49.00000 49.00000
# [6,] 50 50.00000 50.00000 50.00000 41.39189
# [7,] 51 51.00000 51.00000 36.94622 48.29054
# [8,] 52 52.00000 52.00000 42.22425 55.18919
# [9,] 53 53.00000 33.63474 47.50228 62.08783
# [10,] 54 54.00000 37.37193 52.78032 68.98648
# [11,] 55 55.00000 41.10912 58.05835 75.88513
# [12,] 56 56.00000 44.84631 63.33638 82.78378
# [13,] 57 29.86616 48.58351 68.61441 89.68243
# [14,] 58 32.16355 52.32070 73.89244 96.58108
# [15,] 59 34.46095 56.05789 79.17047 103.47972
# [16,] 60 36.75835 59.79509 84.44851 110.37837
# [17,] 61 39.05574 63.53228 89.72654 117.27702
# [18,] 62 41.35314 67.26947 95.00457 124.17567
# [19,] 63 43.65054 71.00666 100.28260 131.07432
# [20,] 64 45.94793 74.74386 105.56063 137.97297
# [21,] 65 48.24533 78.48105 110.83866 144.87161
# [22,] 66 50.54273 82.21824 116.11670 151.77026
# [23,] 67 52.84012 85.95543 121.39473 158.66891
# [24,] 68 55.13752 89.69263 126.67276 165.56756
# [25,] 69 57.43492 93.42982 131.95079 172.46621
# [26,] 26 59.73231 97.16701 137.22882 179.36486
# [27,] 27 62.02971 100.90421 142.50685 186.26350
# [28,] 28 64.32711 104.64140 147.78489 193.16215
# [29,] 29 66.62450 108.37859 153.06292 200.06080
# [30,] 30 68.92190 112.11578 158.34095 206.95945
# [31,] 31 71.21930 115.85298 163.61898 213.85810
# [32,] 32 73.51669 119.59017 168.89701 220.75675
# [33,] 33 75.81409 123.32736 174.17504 227.65539
# [34,] 34 78.11149 127.06456 179.45308 234.55404
# [35,] 35 80.40888 130.80175 184.73111 241.45269
# [36,] 36 82.70628 134.53894 190.00914 248.35134
# [37,] 37 85.00368 138.27613 195.28717 255.24999
# [38,] 38 87.30107 142.01333 200.56520 262.14864
# [39,] 39 89.59847 145.75052 205.84323 269.04728
# [40,] 40 91.89587 149.48771 211.12127 275.94593
答案 1 :(得分：0)
基于上述@IceCreamToucan的答案---我不知道row和col！---您也可以按照以下步骤做很多事情：
map2(row(settlements), col(settlements), FUN) %>%
matrix(nrow = 40, ncol = 5)
不幸的是，如果您想在R中进行功能上的操作，将事物视为向量而不是矩阵要容易得多。幸运的是，矩阵实际上是 向量。困难的部分是找回i和j索引。
由于语法要简单一些，因此我将使用purrr。这有点混乱---如果您希望此代码可重现，我将清理map_int的参数---但基本的想法是执行以下操作：
library(functional)
library(purrr)
# Make a function that can convert back and forth between position in vector
#+ and the indices of the same element in the matrix.
get_indices <- function(nrow, n) {
i <- ((n - 1) %% nrow) + 1
j <- ((n - 1) %/% nrow) + 1
list(i = i, j = j)
}
# Initialize your matrix, but fill it with the position of each element of the
#+ vector.
settlements <- matrix(1:200, nrow = 40, ncol = 5)
# Apply your function, and then turn it back into a matrix
map_dbl(settlements, ~do.call("FUN", Curry(get_indices, nrow = 40)(.))) %>%
matrix(nrow = 40, ncol = 5)