summary.lmList 中的标准误差不是数字 (NaN)

Question

我正在使用 nlme 包中的 Pixel 数据来拟合具有 lmList 函数的模型：

    dat <- lmList(pixel ~ day+I(day^2)|Dog/Side, data=Pixel[Pixel$Dog != 9,], level=2)

我很好奇为什么当我尝试使用 summary 打印拟合对象时，为什么 Dog==10 得到 NaN？

summary(dat)

Call:


Model: pixel ~ day + I(day^2) | Dog/Side 
 Level: 2 
   Data: Pixel[Pixel$Dog != 9, ] 

Coefficients:
   (Intercept) 
     Estimate Std. Error   t value Pr(>|t|)
1/R  1045.349   6.436476 162.41015        0
2/R  1042.166   6.436476 161.91569        0
3/R  1046.265   7.853767 133.21825        0
4/R  1045.602   7.853767 133.13382        0
5/R  1110.309  27.576874  40.26231        0
6/R  1093.556  27.576874  39.65482        0
7/R  1156.478  30.223890  38.26369        0
8/R  1030.754  30.223890  34.10393        0
10/R 1056.600        NaN       NaN      NaN
1/L  1046.538   6.436476 162.59486        0
2/L  1050.367   6.436476 163.18985        0
3/L  1047.438   7.853767 133.36754        0
4/L  1050.915   7.853767 133.81027        0
5/L  1068.412  27.576874  38.74306        0
6/L  1089.184  27.576874  39.49630        0
7/L  1139.851  30.223890  37.71356        0
8/L  1086.129  30.223890  35.93611        0
10/L 1041.100        NaN       NaN      NaN
   day 
        Estimate Std. Error     t value     Pr(>|t|)
1/R   0.21534820   2.600975  0.08279519 9.343899e-01
2/R   3.82436362   2.600975  1.47035789 1.485802e-01
3/R   8.59752235   1.698113  5.06298479 7.828854e-06
4/R  12.18801561   1.698113  7.17738612 6.287493e-09
5/R   4.91365979   6.709441  0.73235013 4.678382e-01
6/R  -0.01159794   6.709441 -0.00172860 9.986286e-01
7/R   0.27908291   7.755457  0.03598536 9.714568e-01
8/R  14.20961055   7.755457  1.83220800 7.369405e-02
10/R 16.10000000        NaN         NaN          NaN
1/L   2.22308391   2.600975  0.85471187 3.973407e-01
2/L   3.31617525   2.600975  1.27497407 2.090100e-01
3/L   6.03985508   1.698113  3.55680313 9.127977e-04
4/L  12.48222079   1.698113  7.35064026 3.512296e-09
5/L  14.13427835   6.709441  2.10662542 4.088737e-02
6/L   7.22757732   6.709441  1.07722501 2.872506e-01
7/L  -0.77719849   7.755457 -0.10021311 9.206304e-01
8/L   3.97248744   7.755457  0.51221835 6.110599e-01
10/L 30.60000000        NaN         NaN          NaN
   I(day^2) 
       Estimate Std. Error    t value     Pr(>|t|)
1/R  -0.0507392  0.1819114 -0.2789227 7.816110e-01
2/R  -0.2228509  0.1819114 -1.2250523 2.270733e-01
3/R  -0.3556849  0.0755204 -4.7097854 2.498505e-05
4/R  -0.4708779  0.0755204 -6.2351082 1.522147e-07
5/R  -0.3510125  0.3639863 -0.9643565 3.401377e-01
6/R  -0.0880891  0.3639863 -0.2420122 8.098952e-01
7/R  -0.1462626  0.4245106 -0.3445440 7.320786e-01
8/R  -0.7429334  0.4245106 -1.7500941 8.707333e-02
10/R -1.6250000        NaN        NaN          NaN
1/L  -0.1649267  0.1819114 -0.9066324 3.695397e-01
2/L  -0.2135152  0.1819114 -1.1737319 2.468167e-01
3/L  -0.2764050  0.0755204 -3.6600044 6.720231e-04
4/L  -0.5425352  0.0755204 -7.1839551 6.150012e-09
5/L  -0.8313144  0.3639863 -2.2839170 2.725859e-02
6/L  -0.5060199  0.3639863 -1.3902170 1.714560e-01
7/L  -0.1847048  0.4245106 -0.4351005 6.656163e-01
8/L  -0.1878769  0.4245106 -0.4425729 6.602428e-01
10/L -1.9500000        NaN        NaN          NaN

Residual standard error: 8.820516 on 44 degrees of freedom

Answer 1

对于 Dog==10，模型精确地遍历每个数据点，这导致 NaN 对于 Std. Error。

summary.lmList 中的标准误差不是数字 (NaN)

Not a Number (NaN) for the standard errors in summary.lmList

r

summary

standard-error