2014-10-28 3 views
0

일부 제약 조건 하에서 여러 개의 모형을 사용하여 회귀 분석을 수행하려고합니다. 수식은 다음과 같을 것입니다. 0 국가에 해당하는 베타 버전과 해당 섹터에 대해 동일한 제약 조건하에 Return ~ Country + Sector.여러 제약이있는 선형 회귀 모형 R

test = lm(Ret ~ Dum.count + Dum.sect + 0 , data=reg.data, weights = weight) 

문제가

test$coefficients 

모든 계수를 (이 분야를 잊어 표시하지 않습니다 "소비자입니다 (데이터가 하단에있는 재생하는 dput) : 코드는 등의 후속이다 자유 재량권 "). 나는 더미 모델을 R에서 자발적으로 생략하여 하나의 더미를 생략하여 절편으로 사용했기 때문에 그 수식에 0을 사용했다. 나는 R은 더미 회귀 분석에 이러한 제약 조건을 적용 기본적으로 생각에도 불구하고, 0으로 베타의 몇 가지를 확인해야합니다 내가

options(contrasts=c('contr.sum', 'contr.sum')) 

를 사용하는 생각의 제약에 관한

.

내 질문은 Ret ~ Dum.Count + Dum.sect의 절편뿐만 아니라 모든 더미 변수에 대한 계수를 얻는 방법은 간단합니다.


데이터 :

structure(list(Ret = c(0, 0, -0.029207812448361, -0.0130948776039107, 
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"Telecom Services", "Utilities", "Energy", "Materials", "Industrials", 
"Consumer Discretionary", "Consumer Staples", "Health Care", 
"Financials", "Telecom Services"), Dum.count = structure(c(79L, 
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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, 
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30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 21L, 21L, 21L, 21L, 
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70L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 29L, 29L, 29L, 
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12L, 12L, 12L, 12L, 32L, 32L, 32L, 32L, 32L, 33L, 33L, 33L, 33L, 
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"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", 
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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, 0.0, 
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")) 

답변

0

나는 결국 답을 발견했다. 대조 (합계)에서 마지막 변수는 절편을 설명하지 않는 단지 -sum (베타)입니다. 이것은 대조적으로 합계 (베타) = 0이기 때문입니다.

감사합니다. R