如何在R中具有多列和缺失值的数据帧上执行Durbin Watson测试

我有一个超过4000列的数据框架，它展示了公司。我想测试每列中出现的时间序列的自相关性。为了测试自相关性，我想对我的dataftame中的每一列进行Durbin-Watson测试

structure(list(Date = structure(c(10960, 10961, 10962, 10963, 
10966, 10967, 10968, 10969, 10970, 10973, 10974, 10975, 10976, 
10977, 10980, 10981, 10982, 10983, 10984, 10987, 10988, 10989, 
10990, 10991, 10994, 10995, 10996, 10997, 10998, 11001), class = "Date"), 
    A.G.L.SJ.INVS...LON..DEAD...13.08.15...ASK.PRICE = c(0.0628930817610063, 
    0.0628930817610063, 0.0628930817610063, 0.0628930817610063, 
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    0.0854700854700855, 0.0854700854700855, 0.0436681222707424, 
    0.0436681222707424, 0.0720720720720721), ABACUS.GROUP.DEAD...18.02.09...ASK.PRICE = c(0.0307692307692308, 
    0.0155038759689922, 0.0152671755725191, 0.0152671755725191, 
    0.0152671755725191, 0.0152671755725191, 0.0211161387631976, 
    0.0239520958083832, 0.0239520958083832, 0.0171428571428571, 
    0.0085348506401138, 0.0141843971631206, 0.0141843971631206, 
    0.0141843971631206, 0.0141843971631206, 0.0141843971631206, 
    0.0141843971631206, 0.0199146514935989, 0.0285714285714286, 
    0.0285714285714286, 0.0285714285714286, 0.0285714285714286, 
    0.0285714285714286, 0.0285714285714286, 0.015748031496063, 
    0.0223285486443381, 0.016, 0.021978021978022, 0.00918836140888208, 
    0.0148148148148148), ABB.R..IRS....ASK.PRICE = c(NA_real_, 
    NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, 
    NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, 
    NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, 
    NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, 
    NA_real_, NA_real_, NA_real_, NA_real_, NA_real_), ABBEY.NATIONAL.DEAD...T.O.SEE.702853...ASK.PRICE = c(0.0103734439834025, 
    0.00424628450106157, 0.00630914826498423, 0.00530503978779841, 
    0.00316622691292876, 0.00788732394366197, 0.00115008625646924, 
    0.00467289719626168, 0.0011997600479904, 0.00374765771392879, 
    0.00130293159609121, 0.0026246719160105, 0.0068259385665529, 
    0.00651465798045603, 0.010752688172043, 0.004062288422478, 
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    0.0040133779264214, 0.00832177531206657, 0.00828729281767956, 
    0.00136518771331058, 0.0104555638536221, 0.00149588631264024, 
    0.00448765893792072, 0.0136260408781226, 0.00573065902578797, 
    0.00917431192660551, 0.00158604282315623), ABBEY.PROTECTION.DEAD...20.01.14...ASK.PRICE = c(NA_real_, 
    NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, 
    NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, 
    NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, 
    NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, 
    NA_real_, NA_real_, NA_real_, NA_real_, NA_real_), ABBEYCREST.DEAD...10.10.14...ASK.PRICE = c(0.04149377593361, 
    0.04149377593361, 0.04149377593361, 0.0401606425702811, 0.0401606425702811, 
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    0.04149377593361, 0.04149377593361, 0.04149377593361), ABBOT.GROUP.DEAD...07.03.08...ASK.PRICE = c(0.028169014084507, 
    0.0188679245283019, 0.0892857142857143, 0.0600858369098712, 
    0.0418410041841004, 0.0165289256198347, 0.0165289256198347, 
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    0.0837209302325581, 0.0754716981132075), ABERDEEN.ASSET.MAN..FULLY.PAID.23.09.05...ASK.PRICE = c(NA_real_, 
    NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, 
    NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, 
    NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, 
    NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, 
    NA_real_, NA_real_, NA_real_, NA_real_, NA_real_), ABERDEEN.ASSET.MAN..NIL.PAID.23.09.05...ASK.PRICE = c(NA_real_, 
    NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, 
    NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, 
    NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, 
    NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, 
    NA_real_, NA_real_, NA_real_, NA_real_, NA_real_)), .Names = c("Date", 
"A.G.L.SJ.INVS...LON..DEAD...13.08.15...ASK.PRICE", "ABACUS.GROUP.DEAD...18.02.09...ASK.PRICE", 
"ABB.R..IRS....ASK.PRICE", "ABBEY.NATIONAL.DEAD...T.O.SEE.702853...ASK.PRICE", 
"ABBEY.PROTECTION.DEAD...20.01.14...ASK.PRICE", "ABBEYCREST.DEAD...10.10.14...ASK.PRICE", 
"ABBOT.GROUP.DEAD...07.03.08...ASK.PRICE", "ABERDEEN.ASSET.MAN..FULLY.PAID.23.09.05...ASK.PRICE", 
"ABERDEEN.ASSET.MAN..NIL.PAID.23.09.05...ASK.PRICE"), row.names = c(NA, 
30L), class = c("data.table", "data.frame"))   

---

如果您能帮助我开发一个带有循环的代码，我将不胜感激。如果缺少值，请另外注意。

您在测试中使用了哪些函数？软件包是car和

durbinWatsonTest（model…）

。但老实说，我不知道该怎么做，这是Durbin Watson可用的最佳形式。

lappy（数据，Durbin WatsonTest）

应该与您所追求的非常接近。@MichaelChirico如果我想建议它找到适当的延迟，有什么建议吗？请在问题后面附上所有相关信息。还要记住，这个网站本身并不是关于统计的。