pas un nombre (NaN) pour les erreurs standard dans summary.lmList

Question

J'utilise Pixel données de nlme package pour adapter un modèle avec lmList fonction:

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

Je suis curieux de savoir pourquoi Pourquoi suis-je NaN pour Dog==10 lorsque j'essaie d'imprimer l'objet ajusté en utilisant summary?

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

Pour Dog==10 le modèle va exactement à travers tous les points de données, ce qui se traduit par NaN pour Std. Error.