我有2个data.frames：
在计算之后，每列（在两个列中）都具有更多的“k”级别。 例如。如果第一个data.frame在每个列{a，b，c}和3个级别中有3个公司，那么它将像{a.1，b.1，c.1，a.2，...，c。 3}，第二个是{bench.1，bench.2，bench.3}
我正在尝试创建一个表格，其中公司为行，两个data.frames之间的回归系数为列。
这是我到目前为止所尝试的内容：
a1 <- data.frame(); b1 <- data.frame(); rs <- data.frame()
# company: the company data.frame (levels included)
# benchmark: the benchmark data.frame (levels included)
# levels: the levels
k <- ncol(company)/levels
l <- 1 - k
for (j in 1:levels) {
l <- l + k
k <- k + k
for (i in l:k) {
mod <- lm(company[,i] ~ benchmark[,j])
a1[i,j] <- mod$coefficients[1]
b1[i,j] <- mod$coefficients[2]
rs[i,j] <- summary(mod)$adj.r.squared
}
}
table <- data.frame('Alpha_Coef' = a1, 'Beta_Coef' = b1, 'Adj.R_Squared' = rs)
我会感激任何帮助。 非常感谢你
编辑： 公司数据框架（4级）
> dput(wsf[1:30, seq(1, 356, 32)])
structure(list(W1free = c(-0.0412000679217938, 0.0880096344938139,
0.050722366154044, -0.0946016947369898, 0.0473490608961103, -0.0316948337480497,
-0.000178721497686476, 0.0330043255435735, -0.0130213571012855,
-0.0210134019518695, 0.00515539322334187, 0.0313594382649759,
-0.0636728947738869, 0.069783976440601, -0.0233500288324757,
0.000310577992801808, -0.0106422243645882, -0.00844960399798073,
0.0207508743372746, -0.00655907030852679, -0.00917878754640728,
0.0422985993411094, -0.0583530401655691, 0.00316447724460396,
0.0548253425520056, -0.0303784828505676, -0.0178226463347567,
0.0141621070101102, 0.0040525928183183, 0.00756095153768141),
W1free.32 = c(-0.0303584128041137, 0.0781259056778406, 0.0543601664896256,
-0.0838929634247089, 0.0168995513463754, -0.00116320770298013,
-0.00661142266491474, 0.00316786967673743, 0.014865018488982,
0.00115744890492633, -0.0133139018524208, 0.00357666761370058,
-0.00984757332103789, 0.0121936464190736, -0.0116509859273921,
0.0214700671630899, -9.18974370460937e-05, -0.0291337664969924,
0.0183613008541499, -0.00623650470163079, 0.00188765595333864,
0.0169715922156494, -0.0202622159786693, 0.00117256873232021,
0.0188193851765861, -0.0330732214410425, 0.0276667753578936,
-0.00912446778879232, -0.00645760419877771, 0.0229548860537124
), W1free.64 = c(-0.041320747786529, 0.0940721256003125,
0.0546722155282992, -0.0776194922127773, -0.0188218918393148,
0.0369018784168118, -0.0159710004093249, 0.00270145290316121,
0.00593858603645259, -0.0018398928749183, -0.0203943374367289,
0.0409079997097826, -0.0302107159471338, 0.00687409495325281,
-0.00278673048827845, 0.00579509928589942, 0.00450071408544868,
-0.0167040789608537, 0.0183985148340536, -0.0140021183403113,
0.00302717840065282, 0.010014802876346, -0.0108024225605952,
0.00664477277427707, -0.00998679780654633, 0.0093455115960892,
-0.0157862445056117, 0.0278399667684328, -0.0124817099428715,
-0.00574730232120169), W2free.7 = c(-0.723798800294376, -1.69462545197809,
-0.322808715747857, 0.983076466907199, 0.601543663890055,
0.00738341380613653, -0.17043040850117, -0.130795012258449,
0.0877012527311328, 0.144654492689526, -0.0233580207307919,
-0.130528770674957, -0.0811443177437124, 0.00455323978774625,
0.0322004951423808, 0.041028915171238, 0.0397928561027241,
-0.0146377819487017, -0.0564390552961818, -0.0157108172121778,
0.0418289577687561, 0.0407167980322358, -0.000920052532455742,
-0.0455497658600707, -0.0588733856819542, 0.00103356528290383,
0.072999787419253, 0.0566294953344014, -0.0139270074576876,
-0.051740788996522), W2free.39 = c(-0.719321984671057, -1.67269117285615,
-0.320838224757322, 0.99401218132634, 0.631713043453369,
0.00311609762638836, -0.215156520118741, -0.148974422352992,
0.0967557896976805, 0.120695957831705, -0.0371356083199943,
-0.0698672528876954, -0.0239853298540248, -0.00317615704745786,
0.0069410289690482, 0.00582109106839021, 0.00118481377532628,
-0.000737420044277918, -0.00796548329789375, -0.000991745340389675,
0.00911571004844975, 0.00143748164424383, -0.00218700317993713,
0.00467672676232776, 0.0132823977786716, 0.00346746468015986,
-0.0284915884601326, -0.0246832031983181, 0.0100296772438092,
0.0157902406702257), W2free.71 = c(-0.743286786555513, -1.68048071636107,
-0.304449505870913, 1.00901538641735, 0.626394565701536,
-0.00615227460862292, -0.212468284990251, -0.146055149093144,
0.0968104699798898, 0.129746894309015, -0.0313266854247715,
-0.0756053816865943, -0.0334202489014552, -0.0195998665482904,
0.010111846958308, 0.0392121845384003, 0.00544740230543033,
-0.0252594118720365, -0.0189333126817274, -0.0200581449364624,
0.00172381708912141, 0.0377707777006468, 0.0253024954648705,
0.00375902559668281, -0.000786565490537995, -0.0183404009358504,
-0.0144607525884234, 0.00635615971857548, -0.01995715314952,
-0.0339339332556635), W3free.14 = c(-0.541971799096092, -0.547254597852541,
-0.351417129791281, -0.079614566169948, 0.190159527265416,
0.369077378264343, 0.344217272166032, 0.307260788133197,
0.335851696193595, 0.389852108956907, 0.508410193658837,
0.454576753894716, 0.0246283521212561, -0.468731535325678,
-0.911314207864435, -1.10081031169091, -0.693894242109856,
-0.176018831349174, 0.268013221596553, 0.584156224372314,
0.483437264811605, 0.31842908645562, 0.211725648202932, 0.0860050283532339,
0.0374128558225933, -0.0175973788383141, -0.104572345794254,
-0.139452927246105, -0.102203406030287, -0.0486154328959697
), W3free.46 = c(-0.551809713896474, -0.554226067631237,
-0.344691113458163, -0.0662397128588583, 0.210840558688842,
0.392862015453828, 0.361004880017924, 0.315708806714992,
0.335952251181492, 0.38343966622271, 0.499384154321493, 0.451678337070302,
0.0251530591544403, -0.472351170164682, -0.920102359468484,
-1.12201190642854, -0.722117102849035, -0.19757656345329,
0.257496857320272, 0.585660993514737, 0.489353812024943,
0.318486723025294, 0.201872000937277, 0.0775858599501224,
0.0511079098302739, 0.0245387675955284, -0.0362827260167453,
-0.0723029499363708, -0.0746172353011349, -0.0670522978529944
), W3free.78 = c(-0.54111517653805, -0.544975921014698, -0.340169990337836,
-0.0694109571903436, 0.196448997132187, 0.3710738269895,
0.339483189903327, 0.305610659199469, 0.344043131600595,
0.405907239122663, 0.527335328093289, 0.469685973096099,
0.02498782328932, -0.485994428238778, -0.940087328347237,
-1.13732999528212, -0.724247605957125, -0.192167012739451,
0.266786191889965, 0.598018937656833, 0.497551375682964,
0.324775953859068, 0.203400777243459, 0.0638344266267895,
0.0231419137529647, -0.0116987397715524, -0.0687128256203292,
-0.0832420106844102, -0.056424343917657, -0.0291186092992814
), W4free.21 = c(0.118918655781396, 0.108718779015937, 0.102775253233938,
0.0828524472480944, 0.0410407234124081, -0.012381397981727,
-0.0746511451766081, -0.126967081904642, -0.138260372435415,
-0.142146232655087, -0.144033266870221, -0.113074511945608,
-0.0670368640581458, -0.0159506388411364, 0.0576398958821921,
0.114505138680487, 0.138987380604024, 0.17298154560344, 0.190728780916805,
0.172497528928508, 0.148714427830889, 0.111056896498362,
0.0322016534964464, -0.0411902802201208, -0.108714452830289,
-0.193403780805043, -0.242468575602981, -0.2518526201175,
-0.226528938042806, -0.16536045623284), W4free.53 = c(0.110582824081414,
0.0990631039125744, 0.092803742919437, 0.07531563115484,
0.0364487613311845, -0.0148493771602927, -0.0766616219694969,
-0.130115230718347, -0.140678702662368, -0.141590690581832,
-0.140856314457181, -0.108167295871846, -0.0604430195571222,
-0.00767211515738517, 0.0691273756544755, 0.129924506626998,
0.154897007967789, 0.186742438470891, 0.201270731261766,
0.177042350952871, 0.147222730892732, 0.105238351658075,
0.0205834624120472, -0.0576989254447123, -0.127161196169879,
-0.214118057849679, -0.26440281808828, -0.269229187033526,
-0.237685035633724, -0.172411380276773), W4free.85 = c(0.118750904966437,
0.105876306066672, 0.0980390856567921, 0.0763519039332738,
0.0332059062273466, -0.0198714852858678, -0.0829673978857073,
-0.13673208596903, -0.147663483183383, -0.150600183457387,
-0.15128240500669, -0.117726475726204, -0.0690026777475602,
-0.0158895662765118, 0.0616902413279015, 0.122399296935057,
0.147735045409867, 0.181929151536837, 0.19882362481352, 0.177391946124788,
0.150509412599221, 0.110259155225688, 0.0273385567351872,
-0.0487018627269797, -0.116082830918324, -0.200781736711009,
-0.248333357539446, -0.252999585674256, -0.222777574439045,
-0.157452460819487)), .Names = c("W1free", "W1free.32", "W1free.64",
"W2free.7", "W2free.39", "W2free.71", "W3free.14", "W3free.46",
"W3free.78", "W4free.21", "W4free.53", "W4free.85"), row.names = c(NA,
30L), class = "data.frame")
基准数据框架（4个级别）：
> dput(wbf[1:30,])
structure(list(W1free = c(-0.0432455343158943, 0.0874909119474779,
0.0571001565877175, -0.0884507864841058, 0.0172050650848323,
-0.00224255555827861, 0.000722631485183195, 0.00993908327736091,
-0.0117418265042027, 0.00534988466357996, -0.000940017698511891,
0.00546041849046642, -0.0103733510325452, 0.00956961911965048,
-0.0113459297172276, 0.0133958729726377, -0.0029416252299541,
-0.011414032468681, 0.0141883947414388, -0.00822524736978799,
0.00316136152084385, 0.00521921925859321, -0.0159379140722575,
0.00678918411451175, 0.0148541387078586, -0.0129222646391021,
-0.00765917374935741, 0.0110142405769407, -0.00458116020357859,
0.0168472964297673), W2free = c(-0.740522709681838, -1.67769972639025,
-0.307890697506331, 1.00015354619176, 0.626337664518445, 0.00505640584038886,
-0.205521408512004, -0.150946308038834, 0.0857142318047878, 0.123609501408063,
-0.0266672430532372, -0.0735840422655566, -0.0316973358168455,
-0.00275393824752691, 0.00718734780106011, 0.00446732335756168,
0.000838919259401042, 0.00297041882635259, 6.04838862344908e-05,
-0.00405610663093049, -0.00221649131469466, 0.00381412534328539,
0.0066934836198532, -0.000657836733240365, -0.00444029910039748,
0.00143349288299151, 0.00265511863053606, -0.000743159102980881,
-0.00661664414444181, -0.00754557691213225), W3free = c(-0.537883232834564,
-0.543185462048805, -0.341173228804443, -0.0719862903967931,
0.195766331288493, 0.373205978054011, 0.344376017839598, 0.312014362375209,
0.346039880023554, 0.403777644728145, 0.52334517757098, 0.46282661064603,
0.0212821638525336, -0.487411032478064, -0.942703893546479, -1.13740960834218,
-0.725250237305827, -0.192667529664807, 0.268680928939938, 0.598063378890107,
0.499012500582589, 0.328774353296679, 0.21316016955454, 0.0826700443418289,
0.0440645204568091, 0.00181643263159242, -0.0732510545397724,
-0.107882422379997, -0.0915052281639699, -0.0597510688882035),
W4free = c(0.120565311954663, 0.110601962226259, 0.105408918589852,
0.0865592864118751, 0.0450732522189251, -0.00895997768433414,
-0.0730317587513421, -0.127472031073358, -0.139664402331508,
-0.144382004669276, -0.14768736817263, -0.118078134288994,
-0.0729438783923505, -0.0220437686110972, 0.0529380577249222,
0.111858330376543, 0.137386196550584, 0.172903566190241,
0.192861994141253, 0.176582979701844, 0.1548325160191, 0.118996625676438,
0.040367522055528, -0.0335387828973377, -0.101185748094811,
-0.187175302200655, -0.238647127560718, -0.250387805121693,
-0.227657278667077, -0.16931414087988)), .Names = c("W1free",
"W2free", "W3free", "W4free"), row.names = c(NA, 30L), class = "data.frame")
答案 0 :(得分：1)
使用Map考虑使用向量化方法，将每个数据框的列名称传递给用户定义的函数 reghandle ，以运行回归并返回模型结果。
但是，要使用Map，长度必须匹配。因此，基准 colnames由 k 级别复制并排序为对齐。最后，函数的返回列表被迭代地转换为数据帧行，然后在do.call(rbind, ...)中绑定在一起：
reghandle <- function(x, y){
mod <- lm(company[[x]] ~ benchmark[[y]])
return(list(Alpha_coef = unname(mod$coefficients[1]),
Beta_coef = unname(mod$coefficients[2]),
Adj.R_Squared = unname(summary(mod)$adj.r.squared)))
}
k <- ncol(company)/levels
benchmarknames <- sort(rep(names(benchmark), k))
benchmarknames
# [1] "W1free" "W1free" "W1free" "W2free" "W2free" "W2free" "W3free" "W3free"
# [9] "W3free" "W4free" "W4free" "W4free"
tablelist <- Map(reghandle, names(company), benchmarknames)
table <- do.call(rbind, lapply(tablelist, data.frame))
table
# Alpha_coef Beta_coef Adj.R_Squared
# W1free -0.0003729282 1.1575992 0.6540945
# W1free.32 0.0003352187 0.9155197 0.8303173
# W1free.64 -0.0002986047 0.9099407 0.6990809
# W2free.7 -0.0003745362 0.9973343 0.9925469
# W2free.39 0.0002856617 0.9957494 0.9991361
# W2free.71 -0.0011951258 1.0032802 0.9986368
# W3free.14 -0.0028038826 0.9759708 0.9991466
# W3free.46 0.0023389397 0.9871707 0.9987852
# W3free.78 0.0009924749 0.9972702 0.9992721
# W4free.21 -0.0012718476 0.9931372 0.9990408
# W4free.53 -0.0036123789 1.0318762 0.9914355
# W4free.85 -0.0029080074 1.0139958 0.9964788