plm函数错误:“名称"属性[343]的长度必须与向量[0]的长度相同 [英] Error in plm function: 'names' attribute [343] must be the same length as the vector [0]
本文介绍了plm函数错误:“名称"属性[343]的长度必须与向量[0]的长度相同的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
我正在使用"plm"功能通过以下代码运行面板回归:
I am running a panel regression using 'plm' function using the following code:
test_reg=plm(y~x1+x2+x3+x4*x7+x5*x7+x6*x7+x8+x9+x10+x11,DATA, index = c("year","id"),model ="within")
summary(test_reg)
然后我得到以下错误:
Error in names(y) <- namesy :
'names' attribute [343] must be the same length as the vector [0]
但是，当我切换y变量和x10变量并再次运行相同的'plm'函数时，我没有收到这样的错误，它的工作原理类似于:
However, when I switch the y variable and x10 variable and run the same 'plm' function again, I do not get such an error and it works well like:
test_reg=plm(x10~x1+x2+x3+x4*x7+x5*x7+x6*x7+x8+x9+y+x11,DATA, index = c("year","id"),model ="within")
summary(test_reg)
数据如下:
Date ID x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 y x11
01/01/2017 1 1 0 0 1 0 0 1 6.5 291.7261837 0.003809784 -0.002609372 0.06258402
01/01/2017 2 0 0 0 1 0 0 1 6.5 291.7261837 0.003809784 -0.002609372 0.06258402
01/01/2017 3 1 0 0 0 1 0 0 7.8 291.7261837 0.005244375 -0.002609372 0.06258402
01/03/2017 4 1 0 0 0 0 0 0 7.8 291.7261837 0.006340987 -0.002609372 0.06258402
01/04/2017 5 0 0 0 1 0 0 1 6.5 291.7261837 0.003923172 0.105154594 0.062638589
01/04/2017 6 0 0 0 1 0 0 1 6.5 291.7261837 0.003923172 0.105154594 0.062638589
01/04/2017 7 0 1 0 1 0 0 0 6.5 291.7261837 0.010499933 0.105154594 0.062638589
01/04/2017 8 0 0 0 0 1 0 0 7.3 291.7261837 0.004619899 0.105154594 0.062638589
01/05/2017 9 0 0 0 0 1 0 0 6.1 291.7261837 0.0069687 -0.16129731 0.062806962
01/05/2017 10 0 0 0 1 0 0 0 7.7 291.7261837 0.006392705 -0.16129731 0.062806962
01/05/2017 11 0 0 0 0 1 0 0 7.3 291.7261837 0.003693392 -0.16129731 0.062806962
01/06/2017 12 1 0 0 1 0 0 1 6.5 291.7261837 0.003951792 -0.070975051 0.06281527
01/06/2017 13 0 1 0 1 0 0 0 6.3 291.7261837 0.006345245 -0.070975051 0.06281527
01/06/2017 14 0 1 0 1 0 0 0 7.8 291.7261837 0.006057317 -0.070975051 0.06281527
01/06/2017 15 0 0 0 0 0 0 0 3.2 291.7261837 0.017651031 -0.070975051 0.06281527
01/07/2017 16 0 1 0 1 0 0 1 6.5 291.7261837 0.003230524 -0.003762958 0.062785401
01/07/2017 17 0 0 0 1 0 0 1 6.5 291.7261837 0.003230524 -0.003762958 0.062785401
01/08/2017 18 0 0 0 1 0 0 1 6.5 291.7261837 0.003575814 -0.003762958 0.062785401
01/09/2017 19 0 0 0 1 0 0 1 6.5 291.7261837 0.003751772 -0.003762958 0.062785401
01/09/2017 20 0 0 0 1 0 0 1 6.5 291.7261837 0.003751772 -0.003762958 0.062785401
01/10/2017 21 0 0 0 1 0 0 1 6.5 291.7261837 0.003236778 0.010738193 0.062756344
01/10/2017 22 1 0 0 0 0 0 0 6.3 291.7261837 0.005293044 0.010738193 0.062756344
01/11/2017 23 0 0 0 1 0 0 1 6.5 291.7261837 0.002724046 -0.159969555 0.062920422
01/11/2017 24 0 0 0 1 0 0 1 6.5 291.7261837 0.002724046 -0.159969555 0.062920422
01/11/2017 25 0 0 0 1 0 0 0 5.8 291.7261837 0.004853874 -0.159969555 0.062920422
01/11/2017 26 0 0 0 0 1 0 0 6.3 291.7261837 0.006511518 -0.159969555 0.062920422
01/12/2017 27 0 1 0 1 0 0 1 6.5 291.7261837 0.002594988 0.046721689 0.062906992
01/12/2017 28 0 1 0 1 0 0 0 7.3 291.7261837 0.003968837 0.046721689 0.062906992
01/13/2017 29 0 0 0 1 0 0 1 6.5 291.7261837 0.002472768 0.028186561 0.062883091
01/13/2017 30 0 0 0 1 0 0 0 6.1 291.7261837 0.007287802 0.028186561 0.062883091
01/13/2017 31 1 0 0 0 0 0 0 6.3 291.7261837 0.004395253 0.028186561 0.062883091
01/13/2017 32 1 0 0 0 0 0 0 6.3 291.7261837 0.004395253 0.028186561 0.062883091
01/13/2017 33 0 0 0 1 0 0 0 7.8 291.7261837 0.00674419 0.028186561 0.062883091
01/14/2017 34 0 0 0 1 0 0 1 6.5 291.7261837 0.002287315 0.003937596 0.062853342
01/14/2017 35 0 0 0 1 0 0 1 6.5 291.7261837 0.002287315 0.003937596 0.062853342
01/14/2017 36 1 0 0 0 0 0 0 3.2 291.7261837 0.016146024 0.003937596 0.062853342
01/15/2017 37 0 1 0 1 0 0 1 6.5 291.7261837 0.002474269 0.003937596 0.062853342
01/15/2017 38 0 0 1 1 0 0 0 7.3 291.7261837 0.005575788 0.003937596 0.062853342
01/16/2017 39 0 0 0 1 0 0 1 6.5 291.7261837 0.002719306 0.003937596 0.062853342
01/16/2017 40 0 0 0 0 1 0 0 7.3 291.7261837 0.008550097 0.003937596 0.062853342
01/16/2017 41 0 1 0 1 0 0 0 5.7 291.7261837 0.006760413 0.003937596 0.062853342
01/17/2017 42 0 1 0 1 0 0 1 6.5 291.7261837 0.002718686 0.078898669 0.062870506
01/17/2017 43 0 1 0 1 0 0 0 5.7 291.7261837 0.006016846 0.078898669 0.062870506
01/17/2017 44 0 0 0 1 0 0 0 7.3 291.7261837 0.005614425 0.078898669 0.062870506
01/17/2017 45 0 0 0 1 0 0 0 5.7 291.7261837 0.004262791 0.078898669 0.062870506
01/18/2017 46 0 0 0 1 0 0 1 6.5 291.7261837 0.002472575 -0.028161673 0.062846684
01/18/2017 47 0 1 0 1 0 0 1 6.5 291.7261837 0.002472575 -0.028161673 0.062846684
01/18/2017 48 0 1 0 1 0 0 1 6.5 291.7261837 0.002472575 -0.028161673 0.062846684
01/20/2017 49 0 0 0 1 0 0 1 6.5 291.7261837 0.002401105 -0.006736635 0.062794741
01/20/2017 50 0 0 0 1 0 0 1 6.5 291.7261837 0.002401105 -0.006736635 0.062794741
01/20/2017 51 0 1 0 1 0 0 1 6.5 291.7261837 0.002401105 -0.006736635 0.062794741
01/20/2017 52 0 0 0 1 0 0 1 6.5 291.7261837 0.002401105 -0.006736635 0.062794741
01/21/2017 53 0 1 0 1 0 0 1 6.5 291.7261837 0.002553359 0.024072255 0.062769421
01/21/2017 54 0 0 0 1 0 0 0 7.3 291.7261837 0.005116216 0.024072255 0.062769421
01/21/2017 55 0 1 0 1 0 0 0 7.8 291.7261837 0.006584331 0.024072255 0.062769421
01/22/2017 56 0 1 0 1 0 0 1 6.5 291.7261837 0.002955839 0.024072255 0.062769421
01/23/2017 57 0 0 0 1 0 0 0 6.5 291.7261837 0.015364925 0.024072255 0.062769421
01/23/2017 58 0 0 0 0 0 1 0 5.2 291.7261837 0.004840524 0.024072255 0.062769421
01/23/2017 59 0 0 1 1 0 0 0 7.8 291.7261837 0.007814092 0.024072255 0.062769421
01/24/2017 60 0 1 0 1 0 0 1 6.5 291.7261837 0.003216472 -0.012183546 0.062740895
01/24/2017 61 0 0 0 1 0 0 1 6.5 291.7261837 0.003216472 -0.012183546 0.062740895
01/25/2017 62 0 0 0 1 0 0 1 6.5 291.7261837 0.003073038 -0.018516248 0.062713872
01/25/2017 63 0 0 0 1 0 0 1 6.5 291.7261837 0.003073038 -0.018516248 0.062713872
01/25/2017 64 0 1 0 1 0 0 1 6.5 291.7261837 0.003073038 -0.018516248 0.062713872
01/25/2017 65 0 0 0 1 0 0 1 6.5 291.7261837 0.003073038 -0.018516248 0.062713872
01/25/2017 66 0 0 0 1 0 0 1 6.5 291.7261837 0.003073038 -0.018516248 0.062713872
01/25/2017 67 0 1 0 1 0 0 0 4.3 291.7261837 0.006130505 -0.018516248 0.062713872
01/25/2017 68 0 1 0 1 0 0 0 7.3 291.7261837 0.005463339 -0.018516248 0.062713872
01/25/2017 69 0 0 0 0 1 0 0 7.2 291.7261837 0.005378501 -0.018516248 0.062713872
01/25/2017 70 0 1 0 0 1 0 0 7.8 291.7261837 0.006395996 -0.018516248 0.062713872
01/26/2017 71 0 0 0 1 0 0 1 6.5 291.7261837 0.003005659 0.025344647 0.06268914
01/26/2017 72 0 0 0 1 0 0 1 6.5 291.7261837 0.003005659 0.025344647 0.06268914
01/26/2017 73 0 1 0 1 0 0 0 8.3 291.7261837 0.005294032 0.025344647 0.06268914
01/27/2017 74 0 0 0 1 0 0 1 6.5 291.7261837 0.003009194 0.004480483 0.062659769
01/27/2017 75 0 1 0 1 0 0 1 6.5 291.7261837 0.003009194 0.004480483 0.062659769
01/27/2017 76 1 0 0 1 0 0 0 5.7 291.7261837 0.005807761 0.004480483 0.062659769
01/27/2017 77 0 0 0 0 1 0 0 6.1 291.7261837 0.006862177 0.004480483 0.062659769
02/01/2017 78 0 0 1 1 0 0 0 6.5 225.4411382 0.011340764 0.017358588 0.062584951
02/02/2017 79 1 0 0 1 0 0 1 6.5 225.4411382 0.002466055 0.026781623 0.062560967
02/02/2017 80 0 1 0 0 1 0 0 5.7 225.4411382 0.007882781 0.026781623 0.062560967
02/03/2017 81 0 0 0 1 0 0 1 6.5 225.4411382 0.002405885 0.011539691 0.062532642
02/03/2017 82 1 0 0 0 1 0 0 7.2 225.4411382 0.005045113 0.011539691 0.062532642
02/03/2017 83 1 0 0 1 0 0 0 7.8 225.4411382 0.003676336 0.011539691 0.062532642
02/04/2017 84 0 0 0 1 0 0 1 6.5 225.4411382 0.002654186 0.010467481 0.06250418
02/04/2017 85 0 1 0 1 0 0 0 6.1 225.4411382 0.006578092 0.010467481 0.06250418
02/04/2017 86 0 1 0 1 0 0 0 7.8 225.4411382 0.003632123 0.010467481 0.06250418
02/05/2017 87 0 0 0 1 0 0 0 7.3 225.4411382 0.005081024 0.010467481 0.06250418
02/06/2017 88 0 0 0 0 0 0 1 6.5 225.4411382 0.003728276 0.010467481 0.06250418
02/06/2017 89 0 0 0 1 0 0 1 6.5 225.4411382 0.003728276 0.010467481 0.06250418
02/06/2017 90 0 1 0 1 0 0 1 6.5 225.4411382 0.003728276 0.010467481 0.06250418
02/06/2017 91 0 0 0 1 0 0 0 6.1 225.4411382 0.007556925 0.010467481 0.06250418
02/07/2017 92 1 0 0 0 1 0 0 6.1 225.4411382 0.00669479 0.025720122 0.062479891
02/07/2017 93 1 0 0 0 0 0 0 6.3 225.4411382 0.005333849 0.025720122 0.062479891
02/07/2017 94 0 0 0 1 0 0 0 5.7 225.4411382 0.005515754 0.025720122 0.062479891
02/07/2017 95 0 1 0 1 0 0 0 7.7 225.4411382 0.00544694 0.025720122 0.062479891
02/07/2017 96 1 0 0 0 0 0 0 7.3 225.4411382 0.004661699 0.025720122 0.062479891
02/07/2017 97 0 0 0 0 0 0 0 7.8 225.4411382 0.003527638 0.025720122 0.062479891
02/08/2017 98 0 1 0 1 0 0 1 6.5 225.4411382 0.00317671 0.009337221 0.06245134
02/08/2017 99 0 1 0 1 0 0 1 6.5 225.4411382 0.00317671 0.009337221 0.06245134
02/08/2017 100 0 0 0 1 0 0 0 6.1 225.4411382 0.00590983 0.009337221 0.06245134
02/08/2017 101 0 1 0 1 0 0 0 7.8 225.4411382 0.002880073 0.009337221 0.06245134
02/08/2017 102 0 0 0 1 0 0 0 7.8 225.4411382 0.002880073 0.009337221 0.06245134
02/09/2017 103 0 1 0 1 0 0 1 6.5 225.4411382 0.003220582 -0.073642932 0.062462725
02/09/2017 104 0 1 0 1 0 0 0 7.7 225.4411382 0.00457101 -0.073642932 0.062462725
02/09/2017 105 0 1 0 1 0 0 0 7.3 225.4411382 0.006184487 -0.073642932 0.062462725
02/09/2017 106 1 0 0 0 1 0 0 6.7 225.4411382 0.007553324 -0.073642932 0.062462725
02/10/2017 107 1 0 0 1 0 0 1 6.5 225.4411382 0.003220913 0.02054262 0.062436737
02/10/2017 108 0 1 0 0 1 0 0 7.3 225.4411382 0.006192293 0.02054262 0.062436737
02/10/2017 109 1 0 0 0 1 0 0 6.3 225.4411382 0.005740194 0.02054262 0.062436737
02/11/2017 110 0 0 0 0 1 0 0 5.8 225.4411382 0.005764743 -0.005847667 0.062407891
02/12/2017 111 1 0 0 0 0 1 0 7.8 225.4411382 0.002921387 -0.005847667 0.062407891
02/13/2017 112 0 0 1 1 0 0 0 6.1 225.4411382 0.007966682 -0.005847667 0.062407891
02/14/2017 113 1 0 0 1 0 0 1 6.5 225.4411382 0.00347653 0.014188136 0.062380333
02/14/2017 114 0 0 0 1 0 0 1 6.5 225.4411382 0.00347653 0.014188136 0.062380333
02/14/2017 115 0 0 0 1 0 0 1 6.5 225.4411382 0.00347653 0.014188136 0.062380333
02/14/2017 116 0 0 0 0 1 0 0 6.1 225.4411382 0.007354973 0.014188136 0.062380333
02/14/2017 117 0 1 0 1 0 0 0 4.3 225.4411382 0.005106887 0.014188136 0.062380333
02/14/2017 118 0 0 0 0 0 1 0 6.5 225.4411382 0.00458087 0.014188136 0.062380333
02/14/2017 119 0 0 0 0 1 0 0 4.5 225.4411382 0.004021296 0.014188136 0.062380333
02/15/2017 120 0 1 0 0 1 0 1 6.5 225.4411382 0.003084593 -0.000418977 0.062351313
02/15/2017 121 0 0 0 1 0 0 0 6.1 225.4411382 0.006590897 -0.000418977 0.062351313
02/15/2017 122 1 0 0 1 0 0 0 7.7 225.4411382 0.004885155 -0.000418977 0.062351313
02/15/2017 123 0 0 0 1 0 0 0 5.2 225.4411382 0.0040034 -0.000418977 0.062351313
02/15/2017 124 1 0 0 0 0 1 0 5.3 225.4411382 0.004399054 -0.000418977 0.062351313
02/16/2017 125 0 0 0 0 1 0 0 7.3 225.4411382 0.004595518 0.022443541 0.062326088
02/17/2017 126 1 0 0 1 0 0 1 6.5 225.4411382 0.002695262 0.022801391 0.062301021
02/17/2017 127 0 0 0 1 0 0 1 6.5 225.4411382 0.002695262 0.022801391 0.062301021
02/18/2017 128 0 0 0 1 0 0 1 6.5 225.4411382 0.002666188 0.005619794 0.062272353
02/19/2017 129 0 0 0 1 0 0 1 6.5 225.4411382 0.002879882 0.005619794 0.062272353
02/19/2017 130 0 1 0 0 0 0 0 7.3 225.4411382 0.005188592 0.005619794 0.062272353
02/20/2017 131 0 1 0 1 0 0 1 6.5 225.4411382 0.003248565 0.005619794 0.062272353
02/21/2017 132 0 1 0 0 1 0 0 6.1 225.4411382 0.007379637 0.048719839 0.062261158
02/22/2017 133 0 0 0 1 0 0 1 6.5 225.4411382 0.002943242 0.013892505 0.062233763
02/22/2017 134 0 0 0 1 0 0 1 6.5 225.4411382 0.002943242 0.013892505 0.062233763
02/22/2017 135 0 0 1 1 0 0 0 6.1 225.4411382 0.006542636 0.013892505 0.062233763
02/22/2017 136 1 0 0 0 0 1 0 6.3 225.4411382 0.003502356 0.013892505 0.062233763
02/23/2017 137 0 1 0 1 0 0 1 6.5 225.4411382 0.002936277 0.019675489 0.062207849
02/23/2017 138 1 0 0 0 1 0 0 7.3 225.4411382 0.005512353 0.019675489 0.062207849
02/24/2017 139 0 0 0 1 0 0 0 6.1 225.4411382 0.006400431 0.018114563 0.062181535
02/24/2017 140 0 0 0 1 0 0 0 6.1 225.4411382 0.006400431 0.018114563 0.062181535
02/25/2017 141 0 1 0 1 0 0 1 6.5 225.4411382 0.002827164 0.014374927 0.062154355
02/25/2017 142 0 0 0 1 0 0 0 7.8 225.4411382 0.002229686 0.014374927 0.062154355
02/27/2017 143 0 1 0 1 0 0 1 6.5 225.4411382 0.003494087 0.014374927 0.062154355
02/27/2017 144 0 0 0 1 0 0 0 6.1 225.4411382 0.008110374 0.014374927 0.062154355
02/27/2017 145 0 0 0 0 1 0 0 6.3 225.4411382 0.008688621 0.014374927 0.062154355
02/27/2017 146 1 0 0 1 0 0 0 4.5 225.4411382 0.013588313 0.014374927 0.062154355
02/27/2017 147 1 0 0 0 1 0 0 5.3 225.4411382 0.004551333 0.014374927 0.062154355
02/28/2017 148 0 1 0 1 0 0 1 6.5 225.4411382 0.003137535 -0.00217941 0.062125715
02/28/2017 149 1 0 0 0 1 0 0 8.3 225.4411382 0.004169185 -0.00217941 0.062125715
03/01/2017 150 0 0 0 1 0 0 1 6.5 238.161619 0.002826907 0.024337051 0.062101474
03/01/2017 151 0 0 0 1 0 0 1 6.5 238.161619 0.002826907 0.024337051 0.062101474
03/01/2017 152 0 0 0 1 0 0 1 6.5 238.161619 0.002826907 0.024337051 0.062101474
03/01/2017 153 0 0 0 1 0 0 0 6.1 238.161619 0.007100868 0.024337051 0.062101474
03/01/2017 154 0 0 0 1 0 0 0 6.1 238.161619 0.007100868 0.024337051 0.062101474
03/01/2017 155 0 0 0 1 0 0 0 6.1 238.161619 0.007100868 0.024337051 0.062101474
03/01/2017 156 0 0 0 1 0 0 0 7.8 238.161619 0.00321752 0.024337051 0.062101474
03/01/2017 157 0 0 0 1 0 0 0 7.8 238.161619 0.00321752 0.024337051 0.062101474
03/01/2017 158 0 0 0 1 0 0 0 7.8 238.161619 0.00321752 0.024337051 0.062101474
03/02/2017 159 0 0 0 1 0 0 1 6.5 238.161619 0.003262569 0.049481385 0.062091033
03/02/2017 160 0 0 0 1 0 0 1 6.5 238.161619 0.003262569 0.049481385 0.062091033
03/02/2017 161 0 0 0 1 0 0 1 6.5 238.161619 0.003262569 0.049481385 0.062091033
03/02/2017 162 0 0 0 1 0 0 1 6.5 238.161619 0.003262569 0.049481385 0.062091033
03/02/2017 163 0 0 0 1 0 0 1 6.5 238.161619 0.003262569 0.049481385 0.062091033
03/02/2017 164 0 0 0 1 0 0 1 6.5 238.161619 0.003262569 0.049481385 0.062091033
03/02/2017 165 0 1 0 1 0 0 0 7.3 238.161619 0.005423948 0.049481385 0.062091033
03/02/2017 166 0 1 0 1 0 0 0 7.3 238.161619 0.005423948 0.049481385 0.062091033
03/02/2017 167 0 1 0 1 0 0 0 7.3 238.161619 0.005423948 0.049481385 0.062091033
03/03/2017 168 1 0 0 1 0 0 1 6.5 238.161619 0.003261096 -0.004402012 0.062062609
03/03/2017 169 1 0 0 1 0 0 1 6.5 238.161619 0.003261096 -0.004402012 0.062062609
03/03/2017 170 1 0 0 1 0 0 1 6.5 238.161619 0.003261096 -0.004402012 0.062062609
03/04/2017 171 0 0 0 1 0 0 0 5.8 238.161619 0.009462591 0.001234096 0.062034093
03/04/2017 172 0 0 0 1 0 0 0 5.8 238.161619 0.009462591 0.001234096 0.062034093
03/04/2017 173 0 0 0 1 0 0 0 5.8 238.161619 0.009462591 0.001234096 0.062034093
03/06/2017 174 0 0 0 1 0 0 1 6.5 238.161619 0.003904071 0.001234096 0.062034093
03/06/2017 175 0 0 0 1 0 0 1 6.5 238.161619 0.003904071 0.001234096 0.062034093
03/06/2017 176 0 0 0 1 0 0 1 6.5 238.161619 0.003904071 0.001234096 0.062034093
03/06/2017 177 0 0 0 1 0 0 1 6.5 238.161619 0.003904071 0.001234096 0.062034093
03/06/2017 178 0 0 0 1 0 0 1 6.5 238.161619 0.003904071 0.001234096 0.062034093
03/06/2017 179 0 0 0 1 0 0 1 6.5 238.161619 0.003904071 0.001234096 0.062034093
03/06/2017 180 0 0 0 1 0 0 0 6.1 238.161619 0.008682166 0.001234096 0.062034093
03/06/2017 181 0 0 0 1 0 0 0 6.1 238.161619 0.008682166 0.001234096 0.062034093
03/06/2017 182 0 0 0 1 0 0 0 6.1 238.161619 0.008682166 0.001234096 0.062034093
03/07/2017 183 0 1 0 1 0 0 1 6.5 238.161619 0.003368821 -0.018231974 0.062008065
03/07/2017 184 0 1 0 1 0 0 1 6.5 238.161619 0.003368821 -0.018231974 0.062008065
03/07/2017 185 0 1 0 1 0 0 1 6.5 238.161619 0.003368821 -0.018231974 0.062008065
03/08/2017 186 1 0 0 1 0 0 0 6.1 238.161619 0.006993576 -0.055975584 0.0620028
03/08/2017 187 1 0 0 1 0 0 0 6.1 238.161619 0.006993576 -0.055975584 0.0620028
03/08/2017 188 1 0 0 1 0 0 0 6.1 238.161619 0.006993576 -0.055975584 0.0620028
03/09/2017 189 0 0 0 1 0 0 0 5.7 238.161619 0.00637861 0.010374975 0.061975174
03/09/2017 190 0 0 0 1 0 0 0 5.7 238.161619 0.00637861 0.010374975 0.061975174
03/09/2017 191 0 0 0 1 0 0 0 5.7 238.161619 0.00637861 0.010374975 0.061975174
03/09/2017 192 0 1 0 1 0 0 0 6.3 238.161619 0.006919775 0.010374975 0.061975174
03/09/2017 193 0 1 0 1 0 0 0 6.3 238.161619 0.006919775 0.010374975 0.061975174
03/09/2017 194 0 1 0 1 0 0 0 6.3 238.161619 0.006919775 0.010374975 0.061975174
03/10/2017 195 0 1 0 1 0 0 0 6.7 238.161619 0.005170876 -0.086404737 0.062001949
03/10/2017 196 0 1 0 1 0 0 0 6.7 238.161619 0.005170876 -0.086404737 0.062001949
03/10/2017 197 0 1 0 1 0 0 0 6.7 238.161619 0.005170876 -0.086404737 0.062001949
03/11/2017 198 0 0 0 1 0 0 0 6.8 238.161619 0.004537821 0.120279563 0.062080276
03/11/2017 199 0 0 0 1 0 0 0 6.8 238.161619 0.004537821 0.120279563 0.062080276
03/11/2017 200 0 0 0 1 0 0 0 6.8 238.161619 0.004537821 0.120279563 0.062080276
03/11/2017 201 0 0 0 0 0 0 0 5.7 238.161619 0.004403423 0.120279563 0.062080276
03/11/2017 202 0 0 0 0 0 0 0 5.7 238.161619 0.004403423 0.120279563 0.062080276
03/11/2017 203 0 0 0 0 0 0 0 5.7 238.161619 0.004403423 0.120279563 0.062080276
03/12/2017 204 0 1 0 0 0 0 1 6.5 238.161619 0.002966075 0.120279563 0.062080276
03/12/2017 205 0 1 0 0 0 0 1 6.5 238.161619 0.002966075 0.120279563 0.062080276
03/12/2017 206 0 1 0 0 0 0 1 6.5 238.161619 0.002966075 0.120279563 0.062080276
03/13/2017 207 0 0 0 1 0 0 0 6.1 238.161619 0.008535417 0.120279563 0.062080276
03/13/2017 208 0 0 0 1 0 0 0 6.1 238.161619 0.008535417 0.120279563 0.062080276
03/13/2017 209 0 0 0 1 0 0 0 6.1 238.161619 0.008535417 0.120279563 0.062080276
03/13/2017 210 0 0 0 1 0 0 0 7.8 238.161619 0.004056711 0.120279563 0.062080276
03/13/2017 211 0 0 0 1 0 0 0 7.8 238.161619 0.004056711 0.120279563 0.062080276
03/13/2017 212 0 0 0 1 0 0 0 7.8 238.161619 0.004056711 0.120279563 0.062080276
03/14/2017 213 0 0 0 1 0 0 1 6.5 238.161619 0.002868652 0.008901032 0.06205248
03/14/2017 214 0 0 0 1 0 0 1 6.5 238.161619 0.002868652 0.008901032 0.06205248
03/14/2017 215 0 0 0 1 0 0 1 6.5 238.161619 0.002868652 0.008901032 0.06205248
03/14/2017 216 0 1 0 1 0 0 0 6.1 238.161619 0.007473939 0.008901032 0.06205248
03/14/2017 217 0 1 0 1 0 0 0 6.1 238.161619 0.007473939 0.008901032 0.06205248
03/14/2017 218 0 1 0 1 0 0 0 6.1 238.161619 0.007473939 0.008901032 0.06205248
03/14/2017 219 1 0 0 1 0 0 0 7.7 238.161619 0.006994721 0.008901032 0.06205248
03/14/2017 220 1 0 0 1 0 0 0 7.7 238.161619 0.006994721 0.008901032 0.06205248
03/14/2017 221 1 0 0 1 0 0 0 7.7 238.161619 0.006994721 0.008901032 0.06205248
03/14/2017 222 0 0 0 1 0 0 0 6.8 238.161619 0.010898931 0.008901032 0.06205248
03/14/2017 223 0 0 0 1 0 0 0 6.8 238.161619 0.010898931 0.008901032 0.06205248
03/14/2017 224 0 0 0 1 0 0 0 6.8 238.161619 0.010898931 0.008901032 0.06205248
03/15/2017 225 0 1 0 1 0 0 1 6.5 238.161619 0.002556912 -0.001296433 0.062024152
03/15/2017 226 0 1 0 1 0 0 1 6.5 238.161619 0.002556912 -0.001296433 0.062024152
03/15/2017 227 0 0 0 1 0 0 1 6.5 238.161619 0.002556912 -0.001296433 0.062024152
03/15/2017 228 0 1 0 1 0 0 1 6.5 238.161619 0.002556912 -0.001296433 0.062024152
03/15/2017 229 0 1 0 1 0 0 1 6.5 238.161619 0.002556912 -0.001296433 0.062024152
03/15/2017 230 0 0 0 1 0 0 1 6.5 238.161619 0.002556912 -0.001296433 0.062024152
03/15/2017 231 0 1 0 1 0 0 1 6.5 238.161619 0.002556912 -0.001296433 0.062024152
03/15/2017 232 0 1 0 1 0 0 1 6.5 238.161619 0.002556912 -0.001296433 0.062024152
03/15/2017 233 0 0 0 1 0 0 1 6.5 238.161619 0.002556912 -0.001296433 0.062024152
03/15/2017 234 0 0 0 1 0 0 0 6.1 238.161619 0.00638285 -0.001296433 0.062024152
03/15/2017 235 0 0 0 1 0 0 0 6.1 238.161619 0.00638285 -0.001296433 0.062024152
03/15/2017 236 0 0 0 1 0 0 0 6.1 238.161619 0.00638285 -0.001296433 0.062024152
03/15/2017 237 0 0 0 0 0 0 0 6.5 238.161619 0.014866991 -0.001296433 0.062024152
03/15/2017 238 0 0 0 0 0 0 0 6.5 238.161619 0.014866991 -0.001296433 0.062024152
03/15/2017 239 0 0 0 0 0 0 0 6.5 238.161619 0.014866991 -0.001296433 0.062024152
03/15/2017 240 0 0 0 1 0 0 0 7.7 238.161619 0.006218225 -0.001296433 0.062024152
03/15/2017 241 0 0 0 1 0 0 0 7.7 238.161619 0.006218225 -0.001296433 0.062024152
03/15/2017 242 0 0 0 1 0 0 0 7.7 238.161619 0.006218225 -0.001296433 0.062024152
04/01/2017 243 0 0 0 1 0 0 1 6.5 192.143176 0.002289305 0.058578409 0.061975502
04/01/2017 244 0 0 0 1 0 0 0 6.1 192.143176 0.005353254 0.058578409 0.061975502
04/02/2017 245 0 0 0 0 1 0 0 6 192.143176 0.003172046 0.058578409 0.061975502
04/03/2017 246 0 0 0 0 0 0 1 6.5 192.143176 0.002848748 0.058578409 0.061975502
04/03/2017 247 0 0 0 0 1 0 0 6.1 192.143176 0.006550344 0.058578409 0.061975502
04/03/2017 248 1 0 0 0 1 0 0 7.3 192.143176 0.007641192 0.058578409 0.061975502
04/03/2017 249 0 0 0 0 0 0 0 7.8 192.143176 0.004299388 0.058578409 0.061975502
04/04/2017 250 0 0 0 1 0 0 1 6.5 192.143176 0.002568017 -0.009146501 0.061948162
04/04/2017 251 0 1 0 0 1 0 0 6.1 192.143176 0.005894783 -0.009146501 0.061948162
04/04/2017 252 0 0 1 1 0 0 0 6.5 192.143176 0.012075672 -0.009146501 0.061948162
04/04/2017 253 0 0 1 1 0 0 0 6.7 192.143176 0.00728987 -0.009146501 0.061948162
04/04/2017 254 0 0 1 1 0 0 0 5.7 192.143176 0.010382549 -0.009146501 0.061948162
04/05/2017 255 0 0 0 0 1 0 1 6.5 192.143176 0.002292826 -0.007230556 0.061920632
04/05/2017 256 0 0 0 0 1 0 0 7.3 192.143176 0.005146235 -0.007230556 0.061920632
04/06/2017 257 1 0 0 1 0 0 1 6.5 192.143176 0.002294755 0.044621463 0.061907235
04/06/2017 258 0 0 0 0 1 0 1 6.5 192.143176 0.002294755 0.044621463 0.061907235
04/06/2017 259 0 0 0 1 0 0 0 6.1 192.143176 0.005304792 0.044621463 0.061907235
04/06/2017 260 0 0 0 0 1 0 0 6.1 192.143176 0.005304792 0.044621463 0.061907235
04/06/2017 261 0 0 0 1 0 0 0 6.1 192.143176 0.005304792 0.044621463 0.061907235
04/06/2017 262 0 0 0 1 0 0 0 6.1 192.143176 0.005304792 0.044621463 0.061907235
04/07/2017 263 0 1 0 1 0 0 1 6.5 192.143176 0.002301431 0.022833827 0.061883181
04/07/2017 264 0 0 0 1 0 0 1 6.5 192.143176 0.002301431 0.022833827 0.061883181
04/08/2017 265 1 0 0 1 0 0 1 6.5 192.143176 0.002278467 0.010542803 0.061856182
04/08/2017 266 1 0 0 1 0 0 1 6.5 192.143176 0.002278467 0.010542803 0.061856182
04/08/2017 267 0 0 0 0 1 0 0 6.1 192.143176 0.00522646 0.010542803 0.061856182
04/09/2017 268 0 1 0 1 0 0 0 7.8 192.143176 0.003720009 0.010542803 0.061856182
04/10/2017 269 0 1 0 1 0 0 1 6.5 192.143176 0.002842283 0.010542803 0.061856182
04/10/2017 270 0 0 0 1 0 0 0 6.1 192.143176 0.006367672 0.010542803 0.061856182
04/10/2017 271 1 0 0 0 1 0 0 6.1 192.143176 0.006367672 0.010542803 0.061856182
04/11/2017 272 0 0 1 1 0 0 0 7.3 192.143176 0.003752301 0.016406649 0.061830366
04/12/2017 273 0 0 0 1 0 0 0 7.8 192.143176 0.003363679 -0.011453291 0.061803585
04/13/2017 274 0 0 0 0 1 0 0 6.1 192.143176 0.004953876 -0.038904987 0.061786866
04/13/2017 275 0 0 0 1 0 0 0 6.1 192.143176 0.004953876 -0.038904987 0.061786866
04/13/2017 276 0 0 0 1 0 0 0 6.1 192.143176 0.004953876 -0.038904987 0.061786866
04/13/2017 277 0 1 0 1 0 0 0 5.7 192.143176 0.005242642 -0.038904987 0.061786866
04/14/2017 278 0 1 0 1 0 0 1 6.5 192.143176 0.00227121 0.010274397 0.061759967
04/14/2017 279 1 0 0 0 1 0 1 6.5 192.143176 0.00227121 0.010274397 0.061759967
04/14/2017 280 0 0 0 1 0 0 0 6.1 192.143176 0.005054546 0.010274397 0.061759967
04/14/2017 281 0 0 0 1 0 0 0 7.8 192.143176 0.00321436 0.010274397 0.061759967
04/15/2017 282 0 1 0 1 0 0 0 4.3 192.143176 0.005754067 0.043387372 0.061472759
05/11/2017 283 0 0 0 0 0 1 0 6.1 193.7951124 0.007689064 0.043387372 0.061472759
05/17/2017 284 0 1 0 1 0 0 1 6.5 193.7951124 0.002436707 0.034476265 0.061448786
05/17/2017 285 0 1 0 1 0 0 0 7.3 193.7951124 0.004911254 0.034476265 0.061448786
05/17/2017 286 1 0 0 0 1 0 0 7.8 193.7951124 0.004096965 0.034476265 0.061448786
05/18/2017 287 1 0 0 0 1 0 1 6.5 193.7951124 0.003189772 0.017341108 0.061423998
05/18/2017 288 0 0 0 1 0 0 1 6.5 193.7951124 0.003189772 0.017341108 0.061423998
05/18/2017 289 0 0 0 1 0 0 1 6.5 193.7951124 0.003189772 0.017341108 0.061423998
05/18/2017 290 0 1 0 1 0 0 1 6.5 193.7951124 0.003189772 0.017341108 0.061423998
05/19/2017 291 0 0 0 1 0 0 1 6.5 193.7951124 0.003154391 0.037870468 0.061407325
05/19/2017 292 0 0 0 1 0 0 0 5.7 193.7951124 0.005666468 0.037870468 0.061407325
05/22/2017 293 1 0 0 1 0 0 1 6.5 193.7951124 0.004100766 0.12857387 0.061498158
05/22/2017 294 0 0 0 1 0 0 0 6.1 193.7951124 0.009390091 0.12857387 0.061498158
05/23/2017 295 0 0 0 1 0 0 0 6.1 193.7951124 0.008675658 -0.004746832 0.061471434
05/24/2017 296 0 0 0 1 0 0 0 4.5 193.7951124 0.005126083 0.099657893 0.061515128
05/25/2017 297 0 0 0 1 0 0 1 6.5 193.7951124 0.003339505 0.035502091 0.061497226
05/25/2017 298 0 0 0 0 1 0 0 6.1 193.7951124 0.007756978 0.035502091 0.061497226
05/25/2017 299 0 0 0 1 0 0 0 6.1 193.7951124 0.007756978 0.035502091 0.061497226
05/25/2017 300 0 0 0 1 0 0 0 6.1 193.7951124 0.007756978 0.035502091 0.061497226
05/26/2017 301 0 1 0 0 1 0 1 6.5 193.7951124 0.003085032 -0.101459299 0.061543369
05/26/2017 302 0 0 0 1 0 0 1 6.5 193.7951124 0.003085032 -0.101459299 0.061543369
05/28/2017 303 0 0 0 1 0 0 1 6.5 193.7951124 0.003484565 -0.000561184 0.061516561
05/28/2017 304 0 0 0 1 0 0 1 6.5 193.7951124 0.003484565 -0.000561184 0.061516561
05/29/2017 305 0 1 0 1 0 0 0 7.8 193.7951124 0.005669722 -0.000561184 0.061516561
05/30/2017 306 0 1 0 1 0 0 0 5.7 193.7951124 0.004592405 -0.002635929 0.061489835
05/31/2017 307 0 1 0 1 0 0 1 6.5 193.7951124 0.003430203 0.016047688 0.061464916
05/31/2017 308 0 1 0 1 0 0 1 6.5 193.7951124 0.003430203 0.016047688 0.061464916
05/31/2017 309 0 0 0 1 0 0 1 6.5 193.7951124 0.003430203 0.016047688 0.061464916
05/31/2017 310 0 0 0 1 0 0 1 6.5 193.7951124 0.003430203 0.016047688 0.061464916
05/31/2017 311 0 1 0 1 0 0 0 5.7 193.7951124 0.004127883 0.016047688 0.061464916
06/01/2017 312 0 0 0 1 0 0 1 6.5 162.775725 0.003417502 0.024917169 0.061442599
06/01/2017 313 0 0 0 1 0 0 0 6.1 162.775725 0.007080266 0.024917169 0.061442599
06/01/2017 314 0 1 0 1 0 0 0 7.3 162.775725 0.00701196 0.024917169 0.061442599
06/02/2017 315 0 0 0 1 0 0 1 6.5 162.775725 0.003507411 0.031259461 0.061422831
06/03/2017 316 0 1 0 1 0 0 1 6.5 162.775725 0.003517714 0.066697292 0.061427601
06/04/2017 317 0 1 0 1 0 0 1 6.5 162.775725 0.003937552 0.066697292 0.061427601
06/05/2017 318 1 0 0 0 0 1 1 6.5 162.775725 0.0043644 0.066697292 0.061427601
06/05/2017 319 0 1 0 1 0 0 0 6.3 162.775725 0.00824234 0.066697292 0.061427601
06/05/2017 320 0 0 0 1 0 0 0 5.7 162.775725 0.008702711 0.066697292 0.061427601
06/06/2017 321 0 0 0 0 1 0 0 6.1 162.775725 0.007922447 0.052613094 0.061420511
06/06/2017 322 1 0 0 0 0 1 0 6.3 162.775725 0.009758651 0.052613094 0.061420511
06/07/2017 323 0 0 0 1 0 0 1 6.5 162.775725 0.003552459 0.02689817 0.061399017
06/07/2017 324 0 0 0 0 1 0 0 6.1 162.775725 0.007161047 0.02689817 0.061399017
06/07/2017 325 0 0 0 1 0 0 0 7.8 162.775725 0.004616254 0.02689817 0.061399017
06/08/2017 326 0 0 0 1 0 0 0 6.1 162.775725 0.007041075 -0.0256721 0.0613771
06/08/2017 327 0 1 0 1 0 0 0 6 162.775725 0.009447309 -0.0256721 0.0613771
06/08/2017 328 0 1 0 1 0 0 0 4.5 162.775725 0.010965946 -0.0256721 0.0613771
06/09/2017 329 0 1 0 1 0 0 1 6.5 162.775725 0.003525695 0.033089851 0.061358282
06/09/2017 330 0 1 0 1 0 0 1 6.5 162.775725 0.003525695 0.033089851 0.061358282
06/09/2017 331 0 0 0 1 0 0 0 6.1 162.775725 0.007052294 0.033089851 0.061358282
06/09/2017 332 0 0 0 1 0 0 0 4.3 162.775725 0.005566463 0.033089851 0.061358282
06/12/2017 333 0 1 0 1 0 0 1 6.5 162.775725 0.004386692 -0.062218475 0.06135903
06/13/2017 334 1 0 0 0 0 1 1 6.5 162.775725 0.003957777 0.029925651 0.061338852
06/14/2017 335 0 0 0 0 0 1 1 6.5 162.775725 0.00353442 -0.072266186 0.061349111
06/14/2017 336 0 0 0 1 0 0 1 6.5 162.775725 0.00353442 -0.072266186 0.061349111
06/14/2017 337 0 0 0 1 0 0 0 6.1 162.775725 0.006936653 -0.072266186 0.061349111
06/14/2017 338 0 0 1 1 0 0 0 7.3 162.775725 0.00475144 -0.072266186 0.061349111
06/14/2017 339 0 1 0 1 0 0 0 7.3 162.775725 0.005299061 -0.072266186 0.061349111
06/15/2017 340 0 1 0 1 0 0 0 6.1 162.775725 0.006977155 -0.093505294 0.061384056
06/15/2017 341 0 0 0 1 0 0 0 8.3 162.775725 0.002227637 -0.093505294 0.061384056
06/15/2017 342 0 0 0 1 0 0 0 7.8 162.775725 0.004187896 -0.093505294 0.061384056
06/15/2017 343 0 0 0 1 0 0 0 7.8 162.775725 0.004187896 -0.093505294 0.061384056
After the error message, I tried using 'traceback()' command and found the following:
After the error message, I tried using 'traceback()' command and found the following:
traceback()
4: pmodel.response.pFormula(formula, data, model = model, effect = effect,
theta = theta)
3: pmodel.response(formula, data, model = model, effect = effect,
theta = theta)
2: plm.fit(formula, data, model, effect, random.method, random.dfcor,
inst.method)
1: plm(y ~ x1 + x2 + x3 + x4 * x7 + x5 * x7 + x6 * x7 +
x8 + x9 + x10 + x11, DATA, index = c("year", "id"), model = "within"
May I ask for help on this?
Or can anyone try this code with this given data and figure out why it is giving such an error?
May I ask for help on this?
Or can anyone try this code with this given data and figure out why it is giving such an error?
推荐答案
There's a couple things going on here, so here's is a multi-part answer.
There's a couple things going on here, so here's is a multi-part answer.
1.Error Explanation
This error occurs because the data contained in your column "y" has many consecutively repeating values. There's only 82 unique "y" values in your sample dataset of 343 rows, and many of those values repeat 3 or 4 times in a row. This presents a problem for estimating a model with plm.
This error occurs because the data contained in your column "y" has many consecutively repeating values. There's only 82 unique "y" values in your sample dataset of 343 rows, and many of those values repeat 3 or 4 times in a row. This presents a problem for estimating a model with plm.
To test this, try filling "y" with values which do not repeat in that way; the error will disappear.
To test this, try filling "y" with values which do not repeat in that way; the error will disappear.
DATA$y <- rnorm(343)
plm(y ~ x1+x2+x3+x4*x7+x5*x7+x6*x7+x8+x9+x10+x11,
DATA,index=c("Date","ID"),model="within")
输出:
Model Formula: y ~ x1 + x2 + x3 + x4 * x7 + x5 * x7 + x6 * x7 + x8 + x9 + x10 + x11
Coefficients:
x1 x2 x3 x4 x7 x5
0.0266245 -0.0085766 0.6009325 -0.2500684 -1.0461660 0.1972594
x6 x8 x10 x4:x7 x7:x5 x7:x6
0.0668570 0.0432360 -85.6184702 0.8044105 0.9602976 0.5484500
As another example, try replacing y with x9, a column which also happens to have a lot of consecutively repeating values. Doing so will produce the same error as in the original "y" case.
As another example, try replacing y with x9, a column which also happens to have a lot of consecutively repeating values. Doing so will produce the same error as in the original "y" case.
2.PLM Application Tips
Now that the main error is explained, there's a few extra points to mention.
Now that the main error is explained, there's a few extra points to mention.
The index argument for plm is typically in the reverse order: index=c("ID","Date") instead of index=c("Date","ID").So that seems like a red flag.I don't know what hypothesis you're testing, but there are a couple tips I can still mention without knowing that.
The index argument for plm is typically in the reverse order: index=c("ID","Date") instead of index=c("Date","ID"). So that seems like a red flag. I don't know what hypothesis you're testing, but there are a couple tips I can still mention without knowing that.
If "Date" is being treated as the ID index and the "ID" column is being treated as the time index, then you'd need to set it up like this:
If "Date" is being treated as the ID index and the "ID" column is being treated as the time index, then you'd need to set it up like this:
Date ID
01/01/2017 1
01/01/2017 2
01/02/2017 1
01/02/2017 2
01/03/2017 1
01/03/2017 2
That scenario seems unlikely though, given that all the IDs in the sample data appear only once. That suggests that this isn't a repeated measures analysis, which means that the order of the index probably should be index=c("ID","Date") and the model probably should be set to "between" not "within" if there's only one observation per "ID" column value. If this second scenario seems true for your case, then try this instead:
That scenario seems unlikely though, given that all the IDs in the sample data appear only once. That suggests that this isn't a repeated measures analysis, which means that the order of the index probably should be index=c("ID","Date") and the model probably should be set to "between" not "within" if there's only one observation per "ID" column value. If this second scenario seems true for your case, then try this instead:
plm(y ~ x1+x2+x3+x4*x7+x5*x7+x6*x7+x8+x9+x10+x11,
DATA,index=c("ID","Date"),model="between")
这篇关于plm函数错误:“名称"属性[343]的长度必须与向量[0]的长度相同的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
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