0 
Losses
[[1]]
[[1]]$`1`
   YearID Category       Loss
1       2    US HU  826.79527
2       4    US HU  357.37926
3       5    US HU  148.71542
4       5    US HU  654.44473
5       8    US HU   81.74428
6       8    US HU   60.27192
7       8    US HU   63.59582
8      10    US HU  778.97650
9      10    US HU 1498.90801
10     11    US HU   66.72708
11     11    US HU  123.25430
12     11    US HU   70.87750
13     11    US HU  272.57985
14     12    US HU   77.14205
15     13    US HU  305.00484
16     16    US HU  577.17381
17     16    US HU  137.12441
18     20    US HU   53.29270
19     20    US HU  123.86174

[[1]]$`2`
   YearID Category        Loss
1       2    US EQ   2.6895238
2       9    US EQ  42.7690970
3       9    US EQ  10.7816904
4      11    US EQ   2.3136544
5      12    US EQ   0.4897316
6      13    US EQ   1.0560520
7      15    US EQ 111.0338641
8      17    US EQ 123.7145577
9      19    US EQ   8.1518931
10     19    US EQ   0.3099158

[[1]]$`3`
  YearID Category       Loss
1      2    JP EQ  0.2920082
2      5    JP EQ 64.6424343
3      7    JP EQ  0.6097201
4      7    JP EQ  6.7342614
5     10    JP EQ  2.9948139
6     10    JP EQ 12.8309552
7     15    JP EQ  0.2373154
8     16    JP EQ  1.6869774
9     20    JP EQ  1.3283421


[[2]]
[[2]]$`1`
   YearID Category       Loss
1      15    US HU  107.63285
2      16    US HU  192.89825
3      19    US HU  215.81496
4       2    US HU  826.79527
5       4    US HU  357.37926
6       5    US HU  148.71542
7       5    US HU  654.44473
8       8    US HU   81.74428
9       8    US HU   60.27192
10      8    US HU   63.59582
11     10    US HU  778.97650
12     10    US HU 1498.90801
13     11    US HU   66.72708
14     11    US HU  123.25430
15     11    US HU   70.87750
16     11    US HU  272.57985
17     12    US HU   77.14205
18     13    US HU  305.00484
19     16    US HU  577.17381
20     16    US HU  137.12441
21     20    US HU   53.29270
22     20    US HU  123.86174

[[2]]$`2`
   YearID Category        Loss
1       5    US EQ   0.6082396
2      10    US EQ 159.8625057
3       2    US EQ   2.6895238
4       9    US EQ  42.7690970
5       9    US EQ  10.7816904
6      11    US EQ   2.3136544
7      12    US EQ   0.4897316
8      13    US EQ   1.0560520
9      15    US EQ 111.0338641
10     17    US EQ 123.7145577
11     19    US EQ   8.1518931
12     19    US EQ   0.3099158

[[2]]$`3`
   YearID Category       Loss
1       7    JP EQ  0.3618008
2      12    JP EQ 11.0977981
3       2    JP EQ  0.2920082
4       5    JP EQ 64.6424343
5       7    JP EQ  0.6097201
6       7    JP EQ  6.7342614
7      10    JP EQ  2.9948139
8      10    JP EQ 12.8309552
9      15    JP EQ  0.2373154
10     16    JP EQ  1.6869774
11     20    JP EQ  1.3283421


[[3]]
[[3]]$`1`
   YearID Category       Loss
1       4    US HU   72.52669
2       8    US HU  270.71636
3      10    US HU  132.25629
4      10    US HU   49.67546
5      11    US HU  134.83450
6      12    US HU  116.10639
7      18    US HU  409.75564
8      19    US HU  268.86671
9      20    US HU  576.44159
10     15    US HU  107.63285
11     16    US HU  192.89825
12     19    US HU  215.81496
13      2    US HU  826.79527
14      4    US HU  357.37926
15      5    US HU  148.71542
16      5    US HU  654.44473
17      8    US HU   81.74428
18      8    US HU   60.27192
19      8    US HU   63.59582
20     10    US HU  778.97650
21     10    US HU 1498.90801
22     11    US HU   66.72708
23     11    US HU  123.25430
24     11    US HU   70.87750
25     11    US HU  272.57985
26     12    US HU   77.14205
27     13    US HU  305.00484
28     16    US HU  577.17381
29     16    US HU  137.12441
30     20    US HU   53.29270
31     20    US HU  123.86174

[[3]]$`2`
   YearID Category        Loss
1       6    US EQ   3.6683023
2      10    US EQ   4.6943200
3      11    US EQ   2.6239743
4      13    US EQ   1.3989827
5      13    US EQ   0.2399834
6      19    US EQ   5.8900987
7       5    US EQ   0.6082396
8      10    US EQ 159.8625057
9       2    US EQ   2.6895238
10      9    US EQ  42.7690970
11      9    US EQ  10.7816904
12     11    US EQ   2.3136544
13     12    US EQ   0.4897316
14     13    US EQ   1.0560520
15     15    US EQ 111.0338641
16     17    US EQ 123.7145577
17     19    US EQ   8.1518931
18     19    US EQ   0.3099158

[[3]]$`3`
   YearID Category        Loss
1       1    JP EQ   0.9199341
2       2    JP EQ   2.7294618
3       7    JP EQ 224.4266519
4      11    JP EQ   0.3948219
5      14    JP EQ   0.7592534
6      15    JP EQ   1.2838320
7       7    JP EQ   0.3618008
8      12    JP EQ  11.0977981
9       2    JP EQ   0.2920082
10      5    JP EQ  64.6424343
11      7    JP EQ   0.6097201
12      7    JP EQ   6.7342614
13     10    JP EQ   2.9948139
14     10    JP EQ  12.8309552
15     15    JP EQ   0.2373154
16     16    JP EQ   1.6869774
17     20    JP EQ   1.3283421

-

list(structure(list(`1` = structure(list(YearID = c(2, 4, 5, 
5, 8, 8, 8, 10, 10, 11, 11, 11, 11, 12, 13, 16, 16, 20, 20), 
    Category = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), class = "factor", .Label = "US HU"), 
    Loss = c(826.795273886473, 357.379257145211, 148.715422723442, 
    654.444727907641, 81.7442792092622, 60.2719188870034, 63.5958204662617, 
    778.976495232166, 1498.90801288032, 66.7270779301846, 123.254304652346, 
    70.8774963289464, 272.579854338023, 77.1420493089197, 305.004836528708, 
    577.173809957773, 137.124412010961, 53.2926961871035, 123.861739643341
    )), .Names = c("YearID", "Category", "Loss"), row.names = c(NA, 
-19L), class = "data.frame"), `2` = structure(list(YearID = c(2, 
9, 9, 11, 12, 13, 15, 17, 19, 19), Category = structure(c(1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), class = "factor", .Label = "US EQ"), 
    Loss = c(2.68952379748490, 42.7690970302631, 10.7816904147645, 
    2.31365444951509, 0.489731609222403, 1.05605200325909, 111.033864050878, 
    123.714557672790, 8.15189312989583, 0.309915764784634)), .Names = c("YearID", 
"Category", "Loss"), row.names = c(NA, -10L), class = "data.frame"), 
    `3` = structure(list(YearID = c(2, 5, 7, 7, 10, 10, 15, 16, 
    20), Category = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L), class = "factor", .Label = "JP EQ"), Loss = c(0.292008160090971, 
    64.6424342762608, 0.609720084708476, 6.73426138743874, 2.99481387294425, 
    12.8309551549701, 0.237315428236897, 1.68697744368254, 1.32834211322205
    )), .Names = c("YearID", "Category", "Loss"), row.names = c(NA, 
    -9L), class = "data.frame")), .Names = c("1", "2", "3")), 
    structure(list(`1` = structure(list(YearID = c(15, 16, 19, 
    2, 4, 5, 5, 8, 8, 8, 10, 10, 11, 11, 11, 11, 12, 13, 16, 
    16, 20, 20), Category = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L), .Label = "US HU", class = "factor"), Loss = c(107.632845241511, 
    192.898254968436, 215.814957575895, 826.795273886473, 357.379257145211, 
    148.715422723442, 654.444727907641, 81.7442792092622, 60.2719188870034, 
    63.5958204662617, 778.976495232166, 1498.90801288032, 66.7270779301846, 
    123.254304652346, 70.8774963289464, 272.579854338023, 77.1420493089197, 
    305.004836528708, 577.173809957773, 137.124412010961, 53.2926961871035, 
    123.861739643341)), .Names = c("YearID", "Category", "Loss"
    ), row.names = c(NA, 22L), class = "data.frame"), `2` = structure(list(
        YearID = c(5, 10, 2, 9, 9, 11, 12, 13, 15, 17, 19, 19
        ), Category = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 
        1L, 1L, 1L, 1L, 1L), .Label = "US EQ", class = "factor"), 
        Loss = c(0.60823959378077, 159.862505662270, 2.68952379748490, 
        42.7690970302631, 10.7816904147645, 2.31365444951509, 
        0.489731609222403, 1.05605200325909, 111.033864050878, 
        123.714557672790, 8.15189312989583, 0.309915764784634
        )), .Names = c("YearID", "Category", "Loss"), row.names = c(NA, 
    12L), class = "data.frame"), `3` = structure(list(YearID = c(7, 
    12, 2, 5, 7, 7, 10, 10, 15, 16, 20), Category = structure(c(1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = "JP EQ", class = "factor"), 
        Loss = c(0.36180083325003, 11.0977980698104, 0.292008160090971, 
        64.6424342762608, 0.609720084708476, 6.73426138743874, 
        2.99481387294425, 12.8309551549701, 0.237315428236897, 
        1.68697744368254, 1.32834211322205)), .Names = c("YearID", 
    "Category", "Loss"), row.names = c(NA, 11L), class = "data.frame")), .Names = c("1", 
    "2", "3")), structure(list(`1` = structure(list(YearID = c(4, 
    8, 10, 10, 11, 12, 18, 19, 20, 15, 16, 19, 2, 4, 5, 5, 8, 
    8, 8, 10, 10, 11, 11, 11, 11, 12, 13, 16, 16, 20, 20), Category = structure(c(1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L
    ), .Label = "US HU", class = "factor"), Loss = c(72.5266949028921, 
    270.716360695826, 132.256285323478, 49.6754599765934, 134.834496124065, 
    116.106394912601, 409.755636985602, 268.866707090942, 576.441585784339, 
    107.632845241511, 192.898254968436, 215.814957575895, 826.795273886473, 
    357.379257145211, 148.715422723442, 654.444727907641, 81.7442792092622, 
    60.2719188870034, 63.5958204662617, 778.976495232166, 1498.90801288032, 
    66.7270779301846, 123.254304652346, 70.8774963289464, 272.579854338023, 
    77.1420493089197, 305.004836528708, 577.173809957773, 137.124412010961, 
    53.2926961871035, 123.861739643341)), .Names = c("YearID", 
    "Category", "Loss"), row.names = c(NA, 31L), class = "data.frame"), 
        `2` = structure(list(YearID = c(6, 10, 11, 13, 13, 19, 
        5, 10, 2, 9, 9, 11, 12, 13, 15, 17, 19, 19), Category = structure(c(1L, 
        1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
        1L, 1L, 1L), .Label = "US EQ", class = "factor"), Loss = c(3.66830233362805, 
        4.69432002882382, 2.62397430771946, 1.39898268940725, 
        0.239983432670597, 5.89009873171407, 0.60823959378077, 
        159.862505662270, 2.68952379748490, 42.7690970302631, 
        10.7816904147645, 2.31365444951509, 0.489731609222403, 
        1.05605200325909, 111.033864050878, 123.714557672790, 
        8.15189312989583, 0.309915764784634)), .Names = c("YearID", 
        "Category", "Loss"), row.names = c(NA, 18L), class = "data.frame"), 
        `3` = structure(list(YearID = c(1, 2, 7, 11, 14, 15, 
        7, 12, 2, 5, 7, 7, 10, 10, 15, 16, 20), Category = structure(c(1L, 
        1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
        1L, 1L), .Label = "JP EQ", class = "factor"), Loss = c(0.91993405093065, 
        2.72946175093358, 224.426651937984, 0.394821850501270, 
        0.75925338029943, 1.28383204511935, 0.36180083325003, 
        11.0977980698104, 0.292008160090971, 64.6424342762608, 
        0.609720084708476, 6.73426138743874, 2.99481387294425, 
        12.8309551549701, 0.237315428236897, 1.68697744368254, 
        1.32834211322205)), .Names = c("YearID", "Category", 
        "Loss"), row.names = c(NA, 17L), class = "data.frame")), .Names = c("1", 
    "2", "3")))

-

InputList = list(c(1.5, 1.2), c(0.9, 0.8), c(1, 1))

の長さInputListは常にの長さと同じになりLossesます。つまり、同じ番号があります。InputListの長さとしてのベクトルのLosses。

InputList最初に、ベクトルの最初の要素に従って昇順で配置する必要があります。

InputList = list(c(0.9, 0.8), c(1, 1),c(1.5, 1.2))

ここで、最初のベクトルの2番目の要素を乗算する必要があります。InputListつまり、ここでは0.8であり、損失の最初のリストにあるすべてのデータフレームの損失列があります。つまり、損失[[1]]

同様に、1にLoss[[2]]のすべてのデータフレームのLoss列を掛けます。

そして、各データフレームの損失列がそれに応じて膨張または収縮しただけで、元のリスト損失と同様のリストとして結果を取得したいと思います。

4

1 に答える 1

2

Q1: 「最初に、ベクトルの最初の要素に従って昇順で InputList を配置する必要があります。つまり、

 newInput <- InputList[order(sapply(InputList, "[[", 1))]
 newInput
[[1]]
[1] 0.9 0.8

[[2]]
[1] 1 1

[[3]]
[1] 1.5 1.2

Q2: "ここで、InputList の最初のベクトルの 2 番目の要素を乗算する必要があります。つまり、ここでは 0.8 であり、Losses の最初のリスト (Losses[[1]]) のすべてのデータ フレームの損失列を乗算し、同様に 1 を乗算します。 Losses[[2]] 内のすべてのデータ フレームの Loss 列

(これは、Losses[[1]] の 3 つのデータフレームすべての Loss 列に 0.9 を掛ける必要があることを意味すると思います。)

これは、データフレームの 3 x 3 マトリックスを返し、それらの Loss 列に目的の係数を掛けたものです。

ddapply <- function(dfrmlist,fac){ 
         lapply(dfrmlist, 
             function(dfrm){ dfrm[, "Loss"] <- dfrm[ ,"Loss"]*fac; return(dfrm)})}
mapply( ddapply,  Losses, sapply(newInput, "[[", 2) )

データフレーム値の最初のセットは、R 行列の要素の列優先順序のため、最初の行ではなく最初の列にあります。したがって、これは Losses の最初の要素の 2 番目の data.frame です。

> mapply( ddapply,  Losses, sapply(newInput, "[[", 2) )[2,1]

$`2`
   YearID Category       Loss
1       2    US EQ  2.1516190
2       9    US EQ 34.2152776
3       9    US EQ  8.6253523
4      11    US EQ  1.8509236
5      12    US EQ  0.3917853
6      13    US EQ  0.8448416
7      15    US EQ 88.8270912
8      17    US EQ 98.9716461
9      19    US EQ  6.5215145
10     19    US EQ  0.2479326
于 2012-06-07T18:11:41.513 に答える