회귀 분석에서의 NA 값의 의미

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나는이 포럼을 수색했지만 적절한 답을 찾지 못했습니다.회귀 분석에서의 NA 값의 의미

나는 lm() 함수를 사용하여 R에 데이터를 맞추려고합니다.

일부 회귀의 순서를 늘리면 일부 계수에 대해 NAs 값이 반환됩니다.

특이 사항 경고가 표시되지 않습니다.

아무도 내가 이것을 해석하는 데 도움이 될 수 있습니까 NAs주세요.

이 예제를 Excel에서 재현 한 것으로 추가하고 6th 피팅에 대한 명백한 문제는 없습니다.

# Data to be fitted 
stock <- c(489197.2129,495385.3703,544941.481,481155.2393,435762.9306,357146.5142,356185.969,368933.2469,293205.1993,247441.8927,183643.7369,166197.9676,157520.3711,192794.255,233347.9248,284770.2749,315789.5782,371253.6707,390370.26,391904.2643,404514.2993,389401.9623,422098.5764,416199.1896,308483.5621,369373.6144,279512.2105,229390.7449,265094.0731,254002.6063,283473.7341,303136.1179,463795.7419,609924.3831,656716.8011,764936.5351,920655.356,944580.607,946031.8681,923518.1477,764901.8927,718968.5814,687068.9807,638677.6326,566646.849,568238.4514,568861.9136,507755.2549,501554.6739,477044.9342,495936.671,463228.2127,453138.3738,480070.4041,545712.6657,577885.5442,611762.2815,601142.7448,514746.6677,461110.9112,380481.1653,386708.9577,387533.3888,387386.6938,410659.1991,556877.152,645031.3825,658208.7034,630659.5014,694707.3162,742454.1986,714466.0588,711948.193,794587.0558,720513.7768,637774.9029,612416.9054,675275.3033,748426.8297,798275.4601,809230.2204,887963.61,976844.1755,951519.2965,984227.0611,1031613.419,1022424.077,954223.6635,955542.5767,920263.327,915265.1223,894546.0728,863364.3203,787588.1825,676158.8976,645554.3779,623867.4582,665281.1634,687277.3358,664088.3827,664183.8062,565105.7676,647452.9797,692280.0696,752396.7808,802440.2146,745383.8062,617119.624,574357.2181,508957.1325,532442.9542,581061.2421,525361.6981,445030.5886,500392.6723,562968.2406,604250.7148,595129.1082,612276.0135,592986.8004,521539.9552,462458.0483,506952.8987,566760.636,652918.6488,735203.327,734013.0201,853466.4269,944496.4215,970809.8151,986741.7525,1051128.913,935281.5542,734450.0057,648969.2357,561200.8083,522026.9359,525941.5821,483971.0487,509913.9941,577511.4155,623777.4897,594309.4065,673049.2068,792564.841,717606.1748,769882.0247,757618.8126,649915.7552,637335.348,643047.1521,584827.5927,594986.2344,616484.6346,682519.916,771948.7174,810259.8153,729710.4213,755624.5028,792269.7553,821344.8099,865256.8204,810055.8891,818226.3348,823462.5172,833754.4351,817210.9106,713206.5412,682863.4516,725996.9946,759555.3406,814912.3816,832986.6209,901940.123,842127.3124,844281.6881,835065.4434,898115.2783,969897.5776,864050.6036,700239.783,740024.1581,688177.6953,720837.362,746235.722,776414.6042,801468.2772,687190.4567,661752.5449,575210.847,657086.9557,741170.8837,667119.5521,716576.0049,798851.6401,925529.028,1113095.162,1170901.816,1245123.324,1118704.034,1112237.559,1077526.324,1071058.372,1056817.839,1170688.049,1147460.898,1034460.346,1103695.303,1237298.977,1158263.736,1152377.973,1216425.62,1259507.168,1230094.739,1303810.435,1271821.888,1489379.948,1497456.885,1373050.412,1354787.605,1361778.393,1153284.081,970091.4123,907278.7659,1119405.724,1167833.909,1106640.238,1089461.159,1227331.968,1291666.639,1286147.056,1206958.849,1067637.209,1020742.997,1011014.154,1115106.152,1057974.568,1056812.59,1058939.012,1020221.806,1077736.49,1110610.504,1110008.193,1114165.552,1088981.295,1112743.615,1090191.485,1035041.804,897884.0154,764819.8401,786705.2071,1047239.939,1041620.036,989888.0243,1052172.174,1142759.928,1202770.868,1354842.679,1386464.631,1284428.757,1227429.952,1191119.178,1220788.93,1337429.916,1484401.276,1541650.876,1578366.413,1696235.083,1487447.482,1326801.659,1366593.384,1377946.19,1322450.948,1306874.043,1368784.977,1439783.001,1511648.674,1652885.159,1744426.741,1730925.901,1808393.081,1842413.869,1695196.299,1716413.086,1656757.555,1391727.704,1431662.545,1352510.013,1445729.796,1498989.601,1468584.044,1355810.704,1283381.625,1341633.292,1401430.509,1479601.908,1345554.608,1360708.621,1547755.19,1510224.416,1836147.717,1933498.647,2003248.251,2039134.04,2197176.515,2273953.329,2119725.123,1883806.835,1796551.502,1826188.718,1937247.67,1790714.402,1797054.648,1570424.416,1499444.414,1472217.319,1574409.675,1576654.181,1428669.458,1443278.681,1453978.398,1724129.787,1653824.594,2010251.997,1964806.887,1795767.829,1819998.743,1848646.139,1795693.654,1626176.807,1510686.457,1742119.961,1577922.47,1568274.698,1399547.138,1450947.192,1390710.466,1485740.833,1772374.119,1903548.298,2207850.281,2297201.405,2153285.523,1935798.325,1991630.36,1861421.8,1792542.838,1846899.839,1981351.805,2068061.716,2148654.112,2108554.414,2050596.262,1899675.419,2142982.747,2152816.563,2284118.374,2187483.675,2193354.528,2358717.33,2528072.391,2451283.945) 
# Produce dates from Jan 1983 to Feb 2013 
t <- seq(1983.1, 2013.2, length = length(stock)) 
# Fit Xt = alpha0 + alpha1^2 + alpha2^3 + alpha3^4 + alpha4^5 + alpha5^6 + eps. 
lm(stock ~ poly(t, 6, raw=TRUE))

결과 : = 원시가 TRUE 사용

Call: 
    lm(formula = stock ~ poly(t, 6, raw = TRUE)) 

Coefficients: 
     (Intercept) poly(t, 6, raw = TRUE)1 poly(t, 6, raw = TRUE)2 
     -3.929e+11    5.265e+08    -1.984e+05 
poly(t, 6, raw = TRUE)3 poly(t, 6, raw = TRUE)4 poly(t, 6, raw = TRUE)5 
       NA    8.352e-03      NA 
poly(t, 6, raw = TRUE)6 
       NA

출처

2013-05-06 tagoma

내가 특이점 표시를 어떻게해야합니까,하지만 난 사용하는 경우에만 '요약()'적합 객체 . – ndoogan

아, 네 말이 맞아. 특이성 경고 (다른 포럼에서 읽은 내용)는 summary() 함수를 사용하지 않고 lm() 함수를 호출하여 읽을 수있을 것이라고 기대합니다. – tagoma

그리고 나서이 결론을 내릴 수 있습니까? 어떻게 대처할 수 있습니까? – tagoma

는 다항식의 고등학교 버전의 사용으로 발생하는 공선 성 문제를 방지하는 폴리 기능의 목적을 패배 우리가 어렸을 때 우리가 배웠던 적합성. 해당 데이터에 더 복잡한 다항식을 원한다면, 원시 = TRUE 드롭 :

lm(stock ~ poly(t, 6)) 

Call: 
lm(formula = stock ~ poly(t, 6)) 

Coefficients: 
(Intercept) poly(t, 6)1 poly(t, 6)2 poly(t, 6)3 poly(t, 6)4 poly(t, 6)5 
    1030800  8866278  2217055  724873  -611351  580051 
poly(t, 6)6 
    721027 

plot(t, stock) 
lines(t, predict(lm(stock ~ poly(t, 6, raw=TRUE)))) 
lines(t, predict(lm(stock ~ poly(t, 6))) , col="red", lwd=3)

enter image description here

출처

2013-05-06 16:37:34

환상적! 감사합니다 DWIN. – tagoma

회귀 분석에서의 NA 값의 의미

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