我试图在一些限制条件下使用多个假人进行回归。公式为：在国家的β总和等于0的约束下返回〜国家+部门，对于该部门则相同。代码如下:(重现数据的输入位于底部）
test = lm(Ret ~ Dum.count + Dum.sect + 0 , data=reg.data, weights = weight)
问题在于
test$coefficients
不显示所有系数（它会忘记扇区“消费者自由裁量权”）。我读到R中的虚拟模型化会忽略一个虚拟用作截距，这就是我在公式中使用0的原因。
关于我正在考虑使用
的约束options(contrasts=c('contr.sum', 'contr.sum'))
哪个应该确保一些beta为0，即使我认为默认情况下R对虚拟回归应用这样的约束。
我的问题很简单，我如何获得所有虚拟变量的系数以及Ret~Dum.Count + Dum.sect中的截距。
数据：
structure(list(Ret = c(0, 0, -0.029207812448361, -0.0130948776039107,
0, -0.0139720566633232, -0.0101638349799049, -0.014567900868859,
-0.0160237311029044, 0, -0.0138193495631563, -0.0118883623673851,
-0.0127607940998118, -0.0168323947578526, -0.0140598414299611,
-0.0270653026036032, -0.013511069247101, -0.0190114076115796,
-0.00954127690170647, -0.00814207809427425, -0.0158862534893693,
0.00250062313018495, -0.015424574198733, -0.0171911400649766,
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-0.0157298360407402, 0.0053586713804914, 0.0179304441180137,
0.00979384741520195, 0.0116018269502725, 0.00122347981174808,
0.0115073954888256, 0.00775992307966877, 0.0121949267497194,
-0.0146997128177213, -0.000215525277190709, -0.00896361197372919,
-0.000835923344706724, -0.000232890994861901, 0.00641661895030676,
-0.0104823974697706, -0.00844271241021, -0.00432712125533785,
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0.0097798264183806, 0.0143961770922494, 0.0252683812565806, 0.00563260340433058,
0.00334287848464543, 0.00835714828430389, 0.0107771256263582,
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0.00792966751267032, -0.0157232672422116, 0.00125884611876703,
0.0310231057254129, 0.00402193467607681, -0.00121009036148767,
0.00022232060186167, 0.0484403657127666, -0.0102214651737076,
-0.0249988098851416, -0.0216788100661882, -0.0137027808902404,
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-0.00280373699740988, -0.0243112060592608, -0.0132383744206145,
0.0106477369754114, 0, 0, -0.013426426522294, -0.0172944774973097,
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0.0153246731111047, 0.00245398972794453, 0, 0, 0, 0, 0, 0, 0,
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-0.0098290573835752, -0.0376134812621233, 0.0180416603872335,
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0.0064401025366938, 0.0213990855365818, 0.0038106323595648, -0.00195721095748969,
0.0147058822269497, 0.0066857684565933, 0.00186540579163852,
-0.00726165400197554, -0.0119383516086875, -0.0164804096531268,
0.00324923087488393, 0.00309000870142828, 0, -0.00738244417262734,
0.00353081443803238, -0.0114724575309201, 0.000107350663112404,
-0.00552486283201059, -0.0152003926399522, -0.00202485399514052,
0.00494151428543499, -0.00760244020239975, 0.000151309270926658,
-0.000995887251685423, -0.00340575234330787, 0.00794552468230658,
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-0.00724354855628362, -0.0203438713533814, 0.00047778086527539,
-0.00191240348648059, -0.00148113348601808, -0.00141339061818291,
-0.00944409014293923), Dum.sect = c("Industrials", "Financials",
"Energy", "Financials", "Telecom Services", "Energy", "Materials",
"Industrials", "Financials", "Telecom Services", "Energy", "Materials",
"Industrials", "Consumer Discretionary", "Consumer Staples",
"Health Care", "Financials", "Information Technology", "Telecom Services",
"Utilities", "Materials", "Consumer Discretionary", "Consumer Staples",
"Health Care", "Financials", "Telecom Services", "Materials",
"Financials", "Telecom Services", "Energy", "Materials", "Industrials",
"Consumer Discretionary", "Consumer Staples", "Health Care",
"Financials", "Information Technology", "Telecom Services", "Utilities",
"Energy", "Materials", "Industrials", "Consumer Discretionary",
"Consumer Staples", "Health Care", "Financials", "Information Technology",
"Telecom Services", "Utilities", "Energy", "Materials", "Industrials",
"Consumer Discretionary", "Consumer Staples", "Health Care",
"Financials", "Telecom Services", "Energy", "Materials", "Industrials",
"Consumer Discretionary", "Consumer Staples", "Financials", "Telecom Services",
"Utilities", "Energy", "Materials", "Industrials", "Consumer Discretionary",
"Consumer Staples", "Health Care", "Financials", "Information Technology",
"Telecom Services", "Utilities", "Energy", "Materials", "Consumer Staples",
"Financials", "Utilities", "Financials", "Telecom Services",
"Utilities", "Materials", "Industrials", "Consumer Discretionary",
"Consumer Staples", "Health Care", "Financials", "Information Technology",
"Telecom Services", "Utilities", "Materials", "Industrials",
"Consumer Discretionary", "Consumer Staples", "Health Care",
"Financials", "Telecom Services", "Financials", "Telecom Services",
"Energy", "Industrials", "Consumer Discretionary", "Consumer Staples",
"Health Care", "Financials", "Information Technology", "Telecom Services",
"Utilities", "Energy", "Materials", "Industrials", "Consumer Discretionary",
"Health Care", "Financials", "Information Technology", "Telecom Services",
"Utilities", "Energy", "Materials", "Industrials", "Consumer Discretionary",
"Consumer Staples", "Health Care", "Financials", "Information Technology",
"Telecom Services", "Utilities", "Energy", "Materials", "Industrials",
"Consumer Discretionary", "Consumer Staples", "Health Care",
"Financials", "Information Technology", "Telecom Services", "Utilities",
"Materials", "Consumer Discretionary", "Financials", "Telecom Services",
"Utilities", "Industrials", "Consumer Discretionary", "Financials",
"Information Technology", "Telecom Services", "Utilities", "Energy",
"Health Care", "Financials", "Energy", "Materials", "Industrials",
"Consumer Discretionary", "Consumer Staples", "Health Care",
"Financials", "Telecom Services", "Utilities", "Materials", "Industrials",
"Consumer Staples", "Financials", "Energy", "Materials", "Health Care",
"Financials", "Information Technology", "Telecom Services", "Energy",
"Materials", "Industrials", "Consumer Discretionary", "Consumer Staples",
"Health Care", "Financials", "Information Technology", "Telecom Services",
"Utilities", "Energy", "Industrials", "Consumer Discretionary",
"Financials", "Telecom Services", "Utilities", "Materials", "Financials",
"Telecom Services", "Energy", "Materials", "Industrials", "Consumer Discretionary",
"Consumer Staples", "Health Care", "Financials", "Information Technology",
"Telecom Services", "Utilities", "Energy", "Materials", "Industrials",
"Consumer Discretionary", "Consumer Staples", "Health Care",
"Financials", "Information Technology", "Telecom Services", "Utilities",
"Industrials", "Financials", "Telecom Services", "Industrials",
"Consumer Staples", "Financials", "Materials", "Financials",
"Telecom Services", "Telecom Services", "Energy", "Materials",
"Industrials", "Consumer Discretionary", "Consumer Staples",
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"Energy", "Materials", "Industrials", "Consumer Discretionary",
"Consumer Staples", "Financials", "Information Technology", "Telecom Services",
"Energy", "Materials", "Consumer Staples", "Financials", "Telecom Services",
"Materials", "Industrials", "Health Care", "Information Technology",
"Telecom Services", "Utilities", "Materials", "Financials", "Telecom Services",
"Materials", "Financials", "Industrials", "Consumer Discretionary",
"Consumer Staples", "Financials", "Telecom Services", "Utilities",
"Energy", "Materials", "Consumer Staples", "Financials", "Telecom Services",
"Utilities", "Energy", "Materials", "Consumer Discretionary",
"Consumer Staples", "Financials", "Telecom Services", "Utilities",
"Energy", "Consumer Staples", "Financials", "Utilities", "Industrials",
"Financials", "Telecom Services", "Utilities", "Energy", "Materials",
"Consumer Staples", "Financials", "Telecom Services", "Utilities",
"Energy", "Materials", "Industrials", "Consumer Discretionary",
"Consumer Staples", "Health Care", "Financials", "Information Technology",
"Telecom Services", "Industrials", "Consumer Discretionary",
"Consumer Staples", "Financials", "Telecom Services", "Energy",
"Materials", "Industrials", "Consumer Discretionary", "Consumer Staples",
"Health Care", "Financials", "Telecom Services", "Utilities",
"Energy", "Materials", "Industrials", "Consumer Discretionary",
"Consumer Staples", "Financials", "Telecom Services", "Energy",
"Materials", "Industrials", "Consumer Discretionary", "Consumer Staples",
"Health Care", "Financials", "Information Technology", "Telecom Services",
"Energy", "Materials", "Industrials", "Consumer Discretionary",
"Consumer Staples", "Health Care", "Financials", "Information Technology",
"Telecom Services", "Utilities", "Energy", "Materials", "Industrials",
"Consumer Discretionary", "Consumer Staples", "Health Care",
"Financials", "Telecom Services"), Dum.count = structure(c(79L,
79L, 3L, 3L, 3L, 5L, 5L, 5L, 5L, 5L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 8L, 8L, 8L, 8L, 8L, 8L, 6L, 6L, 6L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 14L, 14L, 73L, 73L, 73L, 73L, 73L, 73L, 73L, 73L,
15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 16L, 16L, 16L, 16L, 16L,
16L, 16L, 16L, 16L, 16L, 17L, 17L, 17L, 17L, 17L, 20L, 20L, 20L,
30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 21L, 21L, 21L, 21L,
21L, 21L, 21L, 23L, 23L, 70L, 70L, 70L, 70L, 70L, 70L, 70L, 70L,
70L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 29L, 29L, 29L,
29L, 29L, 29L, 29L, 29L, 29L, 29L, 12L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 32L, 32L, 32L, 32L, 32L, 33L, 33L, 33L, 33L,
33L, 33L, 34L, 34L, 34L, 36L, 36L, 36L, 36L, 36L, 36L, 36L, 36L,
36L, 37L, 37L, 37L, 37L, 38L, 38L, 38L, 38L, 38L, 38L, 35L, 35L,
35L, 35L, 35L, 35L, 35L, 35L, 35L, 35L, 39L, 39L, 39L, 39L, 39L,
39L, 42L, 42L, 42L, 41L, 41L, 41L, 41L, 41L, 41L, 41L, 41L, 41L,
41L, 69L, 69L, 69L, 69L, 69L, 69L, 69L, 69L, 69L, 69L, 45L, 45L,
45L, 71L, 71L, 71L, 51L, 51L, 51L, 50L, 48L, 48L, 48L, 48L, 48L,
48L, 48L, 48L, 48L, 52L, 52L, 52L, 52L, 52L, 52L, 52L, 52L, 55L,
55L, 55L, 55L, 55L, 53L, 53L, 53L, 53L, 53L, 53L, 56L, 56L, 56L,
58L, 58L, 59L, 59L, 59L, 59L, 59L, 59L, 57L, 57L, 57L, 57L, 57L,
57L, 60L, 60L, 60L, 60L, 60L, 60L, 60L, 61L, 61L, 61L, 61L, 62L,
62L, 62L, 62L, 64L, 64L, 64L, 64L, 64L, 64L, 72L, 72L, 72L, 72L,
72L, 72L, 72L, 72L, 72L, 66L, 66L, 66L, 66L, 66L, 75L, 75L, 75L,
75L, 75L, 75L, 75L, 75L, 75L, 78L, 78L, 78L, 78L, 78L, 78L, 78L,
74L, 74L, 74L, 74L, 74L, 74L, 74L, 74L, 74L, 81L, 81L, 81L, 81L,
81L, 81L, 81L, 81L, 81L, 81L, 68L, 68L, 68L, 68L, 68L, 68L, 68L,
68L), .Label = c("ACWI", "ACWI + FM", "ARGENTINA", "AUSTRALIA",
"AUSTRIA", "BAHRAIN", "BANGLADESH", "BELGIUM", "BOSNIA-HERZE.",
"BOTSWANA", "BRAZIL", "BRITAIN", "BULGARIA", "CANADA", "CHILE",
"CHINA", "COLOMBIA", "COSTA RICA", "CROATIA", "CZECH", "DENMARK",
"Dev.", "EGYPT", "EM", "ESTONIA", "EUROZONE", "FINLAND", "FM",
"FRANCE", "GERMANY", "GHANA", "GREECE", "HONG KONG", "HUNGARY",
"INDIA", "INDONESIA", "IRELAND", "ISRAEL", "ITALY", "JAMAICA",
"JAPAN", "JORDAN", "KAZAKHSTAN", "KENYA", "KUWAIT", "LEBANON",
"LITHUANIA", "MALAYSIA", "MAURITIUS", "MEXICO", "MOROCCO", "NETHERLANDS",
"NEW ZEALAND", "NIGERIA", "NORWAY", "OMAN", "PAKISTAN", "PERU",
"PHILIPPINES", "POLAND", "PORTUGAL", "QATAR", "ROMANIA", "RUSSIA",
"Serbia", "SINGAPORE", "SLOVENIA", "SOUTH AFRICA", "SOUTH KOREA",
"SPAIN", "SRI LANKA", "SWEDEN", "SWITZERLAND", "TAIWAN", "THAILAND",
"TRINIDAD", "TUNISIA", "TURKEY", "UAE", "UKRAINE", "UNITED STATES",
"VIETNAM", "ZIMBABWE"), class = "factor"), weight = c(NA, 0.000520041385521202,
9.01950553319875e-05, 0.000100591224348651, 5.41621580434692e-05,
0.000167148878114065, 0.000140032197917218, NA, 0.00043289861233755,
3.03216418923979e-05, 0.0017041844684895, 0.00558849753044759,
NA, 0.000532655412075508, 0.00282636184938851, 0.00128555299047677,
0.0158196948568543, 0.000162084131914362, 0.00066973539869799,
0.000442374807565757, 0.0004169308466344, 7.98731009207813e-05,
0.00274454423202768, 0.000292217898089771, 0.000833908749188782,
0.000148992698676594, 5.37002442822141e-06, 2.55035767874359e-05,
1.13844215503653e-05, 0.00197425770290485, 0.00185089458809941,
NA, 0.00073674898431422, 0.00203490652583355, 9.56794065099678e-05,
0.00424438201363887, 0.000437306245555718, 0.000353266337830866,
0.000677331306890789, 0.0109142635212147, 0.00482170736142478,
NA, 0.00212241054424136, 0.00125334951768297, 0.00134049492981561,
0.0154267153937078, 0.000542182688412873, 0.000995453476412365,
0.000489874175993142, 0.000417456462489544, 0.00225274622367484,
NA, 0.00204031743017601, 0.00748408941402412, 0.01238330940116,
0.00606523455844243, 0.000370800808101754, 0.000159812550668776,
0.000187214745647669, NA, 0.000225733316656032, 0.0002152444548593,
0.000301865173738152, 4.12098919897373e-05, 0.000474066528275033,
0.00313691335134659, 0.000654393929077847, NA, 0.00121581726238987,
0.001197014138175, 0.00038575577333429, 0.00845368851837658,
0.00306158774774048, 0.00243686572288116, 0.000892091475960867,
0.000235494113417541, 0.000258004167635095, 9.59520022496746e-05,
0.000526395755998036, 9.49184607846087e-05, 9.46872741803485e-05,
3.12084980957958e-05, 0.00012980482830891, 0.00476274175547434,
NA, 0.00708771065718882, 0.00129721800667729, 0.00451975766039623,
0.00565243144711742, 0.00252204805736615, 0.00150427736450649,
0.00158669914655263, 0.000328481525529262, NA, 0.000223199361310245,
0.000293105007098944, 0.00289127372344326, 0.000596892251968017,
0.000237504201989964, 0.000182415912144681, 7.23719371526633e-05,
0.000621123627831815, NA, 0.000893240221040478, 0.000145324872475037,
0.000191033269383196, 0.00672776172771586, 0.000423632069828311,
0.00189383338550945, 0.00184917767521366, 6.77939415842332e-05,
0.000384070454868823, NA, 0.000112755275328428, 0.000105370182886625,
0.000629423497685844, 0.00083818255773377, 0.000114319753545826,
0.000320949927350397, 0.00420435515895106, 0.00223772646545699,
NA, 0.00504666689511999, 0.00384833173975654, 0.00416684718091077,
0.00636221222504172, 0.00113088254061143, 0.00186618128466519,
0.00161475781397291, 0.0143614055727104, 0.00802003670008823,
NA, 0.00647120211531932, 0.0132138727218262, 0.0077632262791563,
0.0181539373068718, 0.00076652557316303, 0.00409233184302446,
0.00341541300230001, 3.99254525121229e-05, 0.000187576965055149,
0.000466930324658621, 9.51568919880227e-05, 4.8860016813267e-05,
NA, 0.00196158983239875, 0.00695397443067341, 7.20351684946877e-05,
0.000157550759730307, 0.0013218211130744, 5.88088168409117e-05,
6.66613808645955e-05, 0.000111634934200908, 9.06176128855417e-05,
0.000211552540624322, NA, 0.000545166925830964, 0.000383969522519521,
8.98763657941659e-05, 0.001101400648447, 0.000407890167722191,
0.000158514368833466, 0.000487766814995315, NA, 0.000336038030038428,
0.000246298938179364, 5.27943500874004e-05, 0.000149334619314387,
0.00131509126887927, 0.000375748766387963, 6.65736995469907e-05,
0.000101855880933195, 0.000958326601909033, 0.000625100723205665,
NA, 0.000520592846361429, 0.000828228547472056, 0.000644090081901672,
0.00148329626955155, 0.00165203908371526, 0.000236853436982543,
0.000327567632167369, 0.00229629759400016, NA, 0.000600186700977042,
0.00368916150899651, 0.000486625595007798, 0.00174913110881759,
2.14852756405103e-06, 1.88877351370506e-05, 2.17169502061094e-06,
0.000886968652438946, 0.00478392888904646, NA, 0.0167025785098221,
0.00533599115238815, 0.00492026145014813, 0.0156447950402715,
0.0088887680291652, 0.00446376385202905, 0.00189896944038835,
0.000308360589278871, 0.001602731847897, NA, 0.00344494503811641,
0.00102449645908606, 0.000106518784084221, 0.00261827782410162,
0.00658086485475422, 0.000187487928691746, 0.000350981058253314,
NA, 0.000565669044174583, 0.000167158104926062, NA, 3.24612144691137e-06,
1.65397314983294e-05, 2.92443019551012e-05, 0.000102723894438066,
6.25068934519e-05, 0.00114700667444234, 0.00020384708321477,
0.000200803672792674, NA, 0.000422475568607068, 0.00043742008149273,
0.000101612546050514, 0.00154369406250457, 0.000485874486922917,
0.000531241200858085, 0.000173965248036944, 0.000821040079212838,
NA, 0.000807047252299039, 0.00301427353142851, 0.00206653182278063,
0.00116645591661203, 0.0004825912225592, 0.00149636802015173,
0.000460759215243854, 0.000209298828977479, 0.000599307844568033,
0.000493830372946341, 0.00014892762454252, NA, 7.28181078377453e-05,
3.76758311806009e-05, 0.000125680138587701, 4.90027397022612e-05,
1.88919151006188e-05, 8.52061355242569e-05, 4.09084186506651e-05,
0.000219079113625454, 0.000288385843570973, 0.000348544069690578,
4.81093175061434e-05, 9.21007017007808e-05, 0.000475776084159152,
0.000124980433307756, 6.55297072177827e-05, 9.00818802086268e-05,
5.12001601484466e-05, 4.26040356580944e-06, 7.55220608958236e-05,
3.5582285679068e-06, 3.51648567523055e-06, 0.000209192254833606,
0.000241465206244861, 3.69654103688837e-05, 2.823002331492e-05,
0.0010075550797464, 6.23276933356582e-05, 0.000261329408592834,
0.000192605100211058, 9.61743990486272e-05, 0.000147868104076224,
0.000303093749669182, 4.92940275006448e-05, 0.000317785857716085,
0.000130855366785042, 3.93468370069037e-05, 0.00314890740333094,
0.000572625173718403, 0.000440816156284809, 0.000885502377517394,
0.000517136850869312, 5.3199119107723e-05, 0.000111782316973841,
0.000126146103485941, 0.00304486739110657, 0.00136525299366371,
0.000598926363276632, 0.000268314855850743, 0.00385829870490279,
0.00129431171866651, 0.000776474172765253, 0.0012388099447595,
0.000451626342804488, 0.00025774121586828, 0.00302722558858814,
0.000789720628295024, 0.000532217196303663, 0.000280032446527994,
0.000125820189708014, 0.000115737084687623, 0.000245587635855066,
8.63860878885761e-05, 0.000929215478298609, 0.000258576460942922,
3.9032494610663e-05, 8.84170220735865e-05, 9.87724984279264e-05,
0.00024017294507176, 9.19592862675962e-05, 0.000301008650235801,
0.00104699346435116, 0.000210964615046011, 8.3305059790352e-05,
0.00141681961095272, 0.000427130871018164, 0.000592363577363505,
0.000393290141712418, 1.38720200271958e-05, 0.00249035408313262,
0.00794942394089222, 0.000601927018613472, 0.0545833018767897,
0.0181984383536397, 0.0518403941520953, 0.0639238054332242, 0.0473167646788671,
0.0692990561861212, 0.0814822415743463, 0.100755255190792, 0.0131546811074843,
0.0153130438012927, 0.000962333976405987, 0.000902518231967084,
0.000298764773549114, 0.00224948920662978, 0.000464781688997717,
0.00052303475280344, 0.0024182701684607, 0.00111776138190833)), .Names = c("Ret",
"Dum.sect", "Dum.count", "weight"))
答案 0 :(得分：0)
我最终找到了答案。在对比度（总和）下，最后一个变量只是-sum（beta）不考虑截距。这是因为在这种对比下sum（betas）= 0。
由于 [R