0
那种晚上的简单问题...R中多项式的拟合值太短
我得到了模型及其边际效应。
library(mlogit)
library(plm)
pdata<-plm.data(data,index=c("ID","time"))
mldata<-mlogit.data(pdata,choice="ERA",shape="wide")
mlogit.data2<-mlogit(ERA~1|CHINN+INFL+MONEY+OPENNESS+RGDP+RSVS,data=mldata,reflevel="2")
summary(mlogit.data2)
现在我想看看我的模型的拟合值，但我得到一个错误：
> mldata$fitted <- fitted(mlogit.data2,data=mldata)
Error in `[[<-.data.frame`(object, x, value = value) :
replacement has 189 rows, data has 3534
为什么不属于R计算为我所有的数据行的拟合值？有没有可能解决这个问题？ 在?fitted我没有找到太多的帮助。
此外，mlogit软件包描述无济于事。建议的解决方案给出相同的错误消息：
> mldata$fitted <- fitted(mlogit.data2,outcome=TRUE)
Error in `[[<-.data.frame`(object, x, value = value) :
replacement has 189 rows, data has 3534
PS。数据样本（原始数据有589个观察值）：
> dput(data)
structure(list(ID = c("POL", "POL", "POL", "POL", "POL", "POL",
"POL", "POL", "POL", "POL", "POL", "POL", "POL", "POL", "POL",
"POL", "POL", "POL", "POL", "BGR", "BGR", "BGR", "BGR", "BGR",
"BGR", "BGR", "BGR", "BGR", "BGR", "BGR", "BGR", "BGR", "BGR",
"BGR", "BGR", "BGR", "BGR", "BGR", "CYP", "CYP", "CYP", "CYP",
"CYP", "CYP", "CYP", "CYP", "CYP", "CYP", "CYP", "CYP", "CYP",
"CYP", "CYP", "CYP", "CYP", "CYP", "CYP", "CZE", "CZE", "CZE",
"CZE", "CZE", "CZE", "CZE", "CZE", "CZE", "CZE", "CZE", "CZE",
"CZE", "CZE", "CZE", "CZE", "CZE", "CZE", "CZE", "EST", "EST",
"EST", "EST", "EST", "EST", "EST", "EST", "EST", "EST", "EST",
"EST", "EST", "EST", "EST", "EST", "EST", "EST", "EST", "SVN",
"SVN", "SVN", "SVN", "SVN", "SVN", "SVN", "SVN", "SVN", "SVN",
"SVN", "SVN", "SVN", "SVN", "SVN", "SVN", "SVN", "SVN", "SVN",
"SVK", "SVK", "SVK", "SVK", "SVK", "SVK", "SVK", "SVK", "SVK",
"SVK", "SVK", "SVK", "SVK", "SVK", "SVK", "SVK", "SVK", "SVK",
"SVK", "ROM", "ROM", "ROM", "ROM", "ROM", "ROM", "ROM", "ROM",
"ROM", "ROM", "ROM", "ROM", "ROM", "ROM", "ROM", "ROM", "ROM",
"ROM", "ROM", "MLT", "MLT", "MLT", "MLT", "MLT", "MLT", "MLT",
"MLT", "MLT", "MLT", "MLT", "MLT", "MLT", "MLT", "MLT", "MLT",
"MLT", "MLT", "MLT", "LVA", "LVA", "LVA", "LVA", "LVA", "LVA",
"LVA", "LVA", "LVA", "LVA", "LVA", "LVA", "LVA", "LVA", "LVA",
"LVA", "LVA", "LVA", "LVA", "LTU", "LTU", "LTU", "LTU", "LTU",
"LTU", "LTU", "LTU", "LTU", "LTU"), time = c(1993L, 1994L, 1995L,
1996L, 1997L, 1998L, 1999L, 2000L, 2001L, 2002L, 2003L, 2004L,
2005L, 2006L, 2007L, 2008L, 2009L, 2010L, 2011L, 1993L, 1994L,
1995L, 1996L, 1997L, 1998L, 1999L, 2000L, 2001L, 2002L, 2003L,
2004L, 2005L, 2006L, 2007L, 2008L, 2009L, 2010L, 2011L, 1993L,
1994L, 1995L, 1996L, 1997L, 1998L, 1999L, 2000L, 2001L, 2002L,
2003L, 2004L, 2005L, 2006L, 2007L, 2008L, 2009L, 2010L, 2011L,
1993L, 1994L, 1995L, 1996L, 1997L, 1998L, 1999L, 2000L, 2001L,
2002L, 2003L, 2004L, 2005L, 2006L, 2007L, 2008L, 2009L, 2010L,
2011L, 1993L, 1994L, 1995L, 1996L, 1997L, 1998L, 1999L, 2000L,
2001L, 2002L, 2003L, 2004L, 2005L, 2006L, 2007L, 2008L, 2009L,
2010L, 2011L, 1993L, 1994L, 1995L, 1996L, 1997L, 1998L, 1999L,
2000L, 2001L, 2002L, 2003L, 2004L, 2005L, 2006L, 2007L, 2008L,
2009L, 2010L, 2011L, 1993L, 1994L, 1995L, 1996L, 1997L, 1998L,
1999L, 2000L, 2001L, 2002L, 2003L, 2004L, 2005L, 2006L, 2007L,
2008L, 2009L, 2010L, 2011L, 1993L, 1994L, 1995L, 1996L, 1997L,
1998L, 1999L, 2000L, 2001L, 2002L, 2003L, 2004L, 2005L, 2006L,
2007L, 2008L, 2009L, 2010L, 2011L, 1993L, 1994L, 1995L, 1996L,
1997L, 1998L, 1999L, 2000L, 2001L, 2002L, 2003L, 2004L, 2005L,
2006L, 2007L, 2008L, 2009L, 2010L, 2011L, 1993L, 1994L, 1995L,
1996L, 1997L, 1998L, 1999L, 2000L, 2001L, 2002L, 2003L, 2004L,
2005L, 2006L, 2007L, 2008L, 2009L, 2010L, 2011L, 1993L, 1994L,
1995L, 1996L, 1997L, 1998L, 1999L, 2000L, 2001L, 2002L), CHINN = c(-1.863972,
-0.8081098, -0.1129656, -1.168828, -1.168828, -1.168828, -1.168828,
-1.168828, -1.168828, 0.0644257, 0.0644257, 0.0644257, 0.0644257,
0.0644257, 0.0644257, 0.0644257, 0.0644257, 0.0644257, 0.0644257,
NA, -1.168828, -1.168828, -0.905084, -0.905084, -0.905084, -0.905084,
-0.905084, -1.168828, -1.168828, -0.905084, -0.6413398, -0.3775955,
2.175265, 2.439009, 2.439009, 2.439009, 2.439009, 2.439009, -0.1129656,
-0.1129656, -0.1129656, -1.168828, -1.168828, -1.168828, -1.168828,
-1.168828, -1.168828, -1.168828, -0.1129656, 1.384032, 1.647777,
1.911521, 2.175265, 2.439009, 2.439009, 2.439009, 2.439009, NA,
NA, NA, -0.1129656, -0.1129656, -0.1129656, -0.1129656, 0.1507786,
1.647777, 1.911521, 2.175265, 2.439009, 2.439009, 2.439009, 2.439009,
2.439009, 2.439009, 2.439009, 2.439009, NA, NA, NA, 1.911521,
2.175265, 2.439009, 2.439009, 2.439009, 2.439009, 2.439009, 2.439009,
2.439009, 2.439009, 2.439009, 2.439009, 2.439009, 2.439009, 2.439009,
2.439009, NA, NA, NA, -1.168828, -0.1129656, -0.1129656, -0.1129656,
1.120288, 1.120288, 1.120288, 1.384032, 1.647777, 1.911521, 2.175265,
2.439009, 2.175265, 1.911521, 1.647777, 1.384032, NA, NA, NA,
-1.168828, -1.168828, -1.168828, -1.168828, -1.168828, -1.168828,
-1.168828, 0.0644257, 0.0644257, 0.3281699, 0.5919141, 0.8556583,
1.119403, 1.383147, 1.383147, 1.383147, -1.863972, -1.863972,
-1.863972, -1.168828, -1.168828, -1.168828, -1.168828, -1.168828,
-1.168828, 0.0644257, 0.3281699, 1.647777, 1.911521, 2.175265,
2.439009, 2.439009, 2.439009, 2.439009, 2.439009, -1.168828,
-0.1129656, -0.8081098, -1.168828, -1.168828, -1.168828, -1.168828,
-1.168828, -1.168828, -1.168828, -1.168828, 0.3281699, 1.647777,
1.911521, 2.175265, 2.439009, 2.439009, 2.439009, 2.439009, NA,
NA, NA, 1.911521, 2.175265, 2.175265, 2.175265, 2.175265, 2.175265,
2.175265, 2.439009, 2.439009, 2.439009, 2.439009, 2.439009, 2.439009,
2.439009, 2.439009, 2.439009, NA, NA, NA, 2.175265, 2.439009,
2.439009, 2.439009, 2.439009, 2.439009, 2.439009), DEBT = c(NA,
NA, NA, NA, NA, NA, NA, 32.96342571, 41.3785683, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, 13.82140101, 14.93887326, 15.44619896,
NA, NA, NA, NA, NA, 261.728718, 155.7574546, 156.2502269, 160.8780718,
170.5201125, 179.1116438, 180.2266059, 180.5662061, 171.3756799,
96.7356357, 144.1937256, 97.56739053, 102.2997423, 113.2526569,
16.48781937, 14.67202387, 12.70783399, 10.52030143, 10.15356587,
10.17878009, 11.35669558, 13.21886861, 14.87443421, 15.42556465,
18.26772521, 20.10837875, 22.03346153, 23.70805456, 24.19331983,
25.47516365, 30.87178726, 34.99728295, 38.2537664, NA, NA, 9.875827385,
8.8766474, 8.42058185, 4.139266952, 5.505295373, 4.302063581,
4.095615556, 4.817197751, 5.233459351, 5.473257561, 5.864024828,
5.535902971, 4.81287326, 5.66674747, 8.950785028, 9.109930881,
6.942970339, 21.12055362, 18.51055365, 16.84588811, 20.28397162,
20.91442134, 21.48695465, 22.02828204, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
45.37393175, 44.85614255, 37.1317334, 32.91680584, 31.91610243,
30.2380593, 38.24368205, 46.55843081, 45.6102019, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 185.1302222, 192.8527028,
193.5122253, 178.2882862, 170.9141325, 75.8456974, 81.9356132,
82.94168669, 84.04041655, NA, 11.19069767, 14.58974458, 13.26744904,
10.99750203, 9.54700351, 11.97419745, 12.01682427, 13.65810559,
NA, NA, NA, NA, NA, NA, 22.89317326, 42.16345422, 49.88284724,
42.12534325, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), ERA = structure(c(1L,
1L, 1L, 1L, 1L, 4L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 5L, 5L, 5L, 5L, 5L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 6L, 6L, 6L, 6L, 5L, 5L, 5L, 5L, 1L, 3L, 3L, 3L,
2L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 1L, 1L,
1L, 1L, 1L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 6L, 6L, 6L, 6L,
6L, 5L, 5L, 5L, 5L, 5L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L,
4L, 6L, 6L, 6L, 2L, 2L, 2L, 2L, 1L, 3L, 3L, 3L, 4L, 4L, 4L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 2L, 2L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 2L, 5L, 5L,
5L, 5L, 6L, 6L, 6L, 6L, 6L), .Label = c("0", "1", "2", "4", "5",
"6"), class = "factor"), INFL = c(32.88153432, 30.75869771, 26.06227768,
19.17945154, 14.8181395, 11.13536692, 7.083028472, 10.73214358,
4.365764296, 0.468914489, -0.309213178, 2.505644136, 1.488838798,
0.802931491, 0.757724135, 3.575611747, 2.645957976, 1.673380405,
3.027484539, 68.89452067, 93.56390749, 60.04541489, 120.9697303,
1058.110374, 18.08247302, 2.381037536, 10.98858826, 6.23546693,
4.378871042, 1.059975277, 5.275230048, 4.420631575, 6.949600859,
6.772151187, 11.5750056, 1.573166761, 1.404937188, 2.989021435,
0.869895952, 2.205158532, 0.606399087, 2.341155169, 3.341867083,
1.637920558, 1.438028472, 4.813986108, 0.851572796, 1.369895581,
3.041933627, 1.215313972, 1.940240026, 2.183862478, 0.742157177,
3.895404593, -0.805939925, 1.347186409, 2.058600601, NA, 7.470554816,
7.160886884, 8.162230304, 8.284316757, 10.03680087, 1.951158973,
4.57516348, 3.580328061, 0.353839829, -0.989422969, 1.756368044,
1.227958743, 2.216092544, 1.297107905, 5.5772198, -0.135208014,
0.37464597, 0.705540354, 85.82767831, 45.16125984, 26.76719493,
22.41257365, 10.31840157, 7.618546565, 3.104139369, 4.69827344,
4.613783954, 2.140023859, 0.241157716, 1.977168612, 3.471477961,
4.117902416, 4.967303028, 9.59183654, -1.264796394, 1.941508375,
3.746765989, 28.87406262, 18.50010871, 11.45351537, 9.15451517,
8.098292364, 7.323560048, 5.957481952, 9.551132139, 7.297001041,
6.038828058, 4.4816701, 2.5181237, 1.859242437, 2.150549111,
1.980829766, 4.878079781, -0.324099786, 0.806893393, 0.579731127,
NA, 10.9167027, 7.877750089, 5.171485258, 5.846056108, 6.108485138,
10.37847422, 12.70810524, 6.204147329, 1.892051179, 7.457011294,
6.477597786, 2.090872986, 4.171318751, 1.12638815, 3.824413275,
0.43508474, -0.077053821, 2.688437197, 251.1825911, 134.265962,
30.23306593, 38.19153164, 154.5000031, 58.50679599, 45.6118096,
46.33892251, 33.34228373, 21.10595053, 14.17683938, 10.80596477,
8.370844841, 6.27257596, 3.205443818, 7.074558656, 4.407643597,
5.060143833, 4.556928586, 0.160548827, 1.636491038, 2.41722671,
1.416230304, 2.848600559, 1.795464586, 1.94279736, 3.041921336,
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1.491056375, 104.7834598, 33.43166158, 22.97001407, 16.97350899,
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0.491817452, 1.859491465, 5.116514248, 6.124960356, 6.22186537,
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406.256717, 69.65670824, 37.64737798, 23.9803578, 8.614412981,
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), MONEY = c(33.47353185, 31.98627557, 31.69277212, 33.19737536,
35.11881075, 37.70207735, 40.69940688, 40.60334482, 44.4362372,
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47.82156081, 52.36351657, 53.71374196, 55.42878357, 57.87980215,
78.96788223, 79.70281583, 67.02978372, 83.30492066, 35.17443875,
28.90920096, 30.99875517, 35.97481045, 40.92721065, 41.75303846,
46.25983651, 51.21172707, 55.53534932, 61.91482825, 69.93832611,
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131.4806475, 128.6485046, 136.9487074, 145.3864396, 147.0066966,
159.3015, 161.2947176, 169.5276212, 175.6224217, 174.5080264,
171.4267582, 176.9652918, 189.8620473, 206.7065572, NA, NA, NA,
NA, 60.92063478, 63.44783757, 70.78822385, 65.59993188, 62.32748907,
58.90282855, 57.98895591, 63.69128011, 65.63634827, 55.65605855,
56.64938189, 55.81345513, 58.23563505, 61.13655108, 64.9819949,
70.24647315, 72.07127255, 72.80557549, 74.20307208, NA, NA, 16.28198341,
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31.03625086, 31.54308533, 31.64045263, 57.85539671, 62.89230872,
61.42038589, 56.4714398, 56.99786929, 60.17466759, 59.63639818,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, 61.15405566, 59.60914754, 60.66020951, 63.23684312,
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63.50001349, 54.86765959, 56.68681216, 54.68544393, 56.32768785,
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30.58119755, 34.33863042, 31.56100706, 32.83360067, 33.82383876,
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51.41357898, 46.66134275, 23.06471096, 25.77555608, 20.86731738,
16.09189946, 17.94874959, 18.49845167, 20.44411024, 22.86014763,
26.19698213, 28.50868349), OPENNESS = c(41.05223192, 41.26984163,
44.2465201, 46.04792205, 50.74269481, 56.79411951, 54.29510521,
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74.91126836, 82.51673772, 84.38852522, 83.76331224, 78.80572562,
85.68773797, 91.29238718, 84.04148545, 90.72012785, 101.8271665,
116.1971199, 112.0938612, 117.1780783, 116.5593551, 106.2621055,
106.8660521, 102.9803349, 107.3846266, 115.3229367, 96.16403889,
140.00543, 138.6499918, 136.949385, 103.8483695, 116.7151556,
132.2741747, 95.41637402, 95.77615252, 100.1037057, 104.4416258,
105.3995874, 100.5282431, 101.757892, 109.8712906, 109.9020613,
103.0649958, 95.17382146, 98.02155335, 99.20612861, 99.76019554,
102.1400302, 101.6254989, 85.82792399, 86.67220155, NA, 94.12769898,
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107.2165817, 124.0086884, 126.7486932, 116.447208, 119.381669,
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113.8718253, 129.8170346, 141.6332736, NA, NA, 143.7179084, 134.7434335,
154.3553107, 159.1496939, 145.7897864, 172.7948897, 162.1300543,
149.2192177, 145.8703769, 153.2040725, 161.9353192, 155.6454263,
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116.4165437, 115.2014835, 101.1237517, 100.776851, 103.4629999,
103.6353427, 98.64583785, 110.8680986, 111.1888697, 108.9876134,
107.8240756, 116.9086478, 124.764116, 133.598327, 140.8368678,
137.5052976, 115.3428892, 130.2847534, 141.5175657, 116.9348272,
113.2022693, 113.3092331, 117.4438733, 122.3418682, 129.1833372,
126.8180934, 143.4736581, 153.5098819, 149.5165658, 153.6020099,
151.8684993, 157.1687775, 173.0136412, 174.8163254, 169.3228654,
142.5879841, 163.8001325, 175.7176224, 51.00369936, 51.86707001,
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"RESERVES", "RGDP", "RSVS"), row.names = c(NA, 200L), class = "data.frame")
将'na.action'设置为'na.exclude'可能是票（如果'mlogit'允许'na.exclude'作为选项）。您也可以创建一个NA值被移除的数据集，然后使用它来拟合模型。 – aosmith 2014-09-25 19:31:44