[R] nlme predict with se?

[Dr. Emilio A. Laca]

I am trying to make predictions with se's using a nlme (kew11.nlme  
below).  I get an error indicating levels for a factor are not allowed.
I have searched and read Rnews, MEMSS, MASS, R-Help, and other lists  
in Spanish where I found questions similar to mine but not solution.  
I do not really care about the method used. Any suggestions to obtain  
predictions with se's from an nlme would be appreciated.
eal


R Version 2.2.1  (2005-12-20 r36812)
mac osx 10.4.5 on G5 DP

 > predict(kew11.nlme,x.for.lw.pred, se=T)
Error in predict.nlme(kew11.nlme, x.for.lw.pred, se = T) :
	Levels 1. Fall-Winter,2. Spring,3. Summer,4. Fall not allowed for  
Season

 > x.for.lw.pred[1:10,]
            Season     ecoreg MBreed ageyr2 sheep    farm
1  1. Fall-Winter     desert   Meat   0.60     1 AksToKa
2       2. Spring     desert   Meat   1.00     1 AksToKa
3       3. Summer     desert   Meat   1.25     1 AksToKa
4         4. Fall     desert   Meat   1.50     1 AksToKa
5  1. Fall-Winter  foothills   Half   0.60     1 AksToKa
6       2. Spring  foothills   Half   1.00     1 AksToKa
7       3. Summer  foothills   Half   1.25     1 AksToKa
8         4. Fall  foothills   Half   1.50     1 AksToKa
9  1. Fall-Winter  foothills   Meat   0.60     1 AksToKa
10      2. Spring  foothills   Meat   1.00     1 AksToKa


 > kew11.nlme$call
nlme.formula(model = lw ~ SSasympOff(ageyr2, mw, lgr, age0),
     data = kew, fixed = list(mw + lgr + age0 ~ Season * MBreed +
         ecoreg + ecoreg:Season), random = list(farm = list(mw ~
         1, lgr ~ 1), sheep = list(mw ~ 1)), start = c(fixef 
(kew8.nlme)[1:15],
         0, 0, 0, 0, 0, 0, 0, 0, 0, fixef(kew8.nlme)[16:30], 0,
         0, 0, 0, 0, 0, 0, 0, 0, fixef(kew8.nlme)[31:45], 0, 0,
         0, 0, 0, 0, 0, 0, 0), correlation = corSymm())


 > levels(kew$Season)==levels(x.for.lw.pred$Season)
[1] TRUE TRUE TRUE TRUE


 > kew[1:10,]
Grouped Data: lw ~ ageyr2 | farm
     X sheep doe   ageyr2        MBreEco         Season     ecoreg     
farm  village Breed MBreed     date doy   lw      ebw
1   1     1   1 2.500000 Meatsemidesert 1. Fall-Winter semidesert  
AksToKa Aksenger   KZP   Meat 11/23/02 327 63.8 55.63211
2   2     1 139 2.883333 Meatsemidesert      2. Spring semidesert  
AksToKa Aksenger   KZP   Meat  4/10/03 100 51.7 44.53119
3   3     1 250 3.191667 Meatsemidesert      3. Summer semidesert  
AksToKa Aksenger   KZP   Meat  7/30/03 211 58.3 50.58624
4   4     1 330 3.413889 Meatsemidesert        4. Fall semidesert  
AksToKa Aksenger   KZP   Meat 10/18/03 291 59.9 52.05413
5   5     2   1 6.000000 Meatsemidesert 1. Fall-Winter semidesert  
AksToKa Aksenger   KZP   Meat 11/23/02 327 58.0 50.31101
6   6     2 139 6.383333 Meatsemidesert      2. Spring semidesert  
AksToKa Aksenger   KZP   Meat  4/10/03 100 41.2 34.89817
7   7     2 250 6.691667 Meatsemidesert      3. Summer semidesert  
AksToKa Aksenger   KZP   Meat  7/30/03 211 53.3 45.99908
8   8     2 330 6.913889 Meatsemidesert        4. Fall semidesert  
AksToKa Aksenger   KZP   Meat 10/18/03 291 63.7 55.54037
9   9     3   1 4.000000 Meatsemidesert 1. Fall-Winter semidesert  
AksToKa Aksenger   KZP   Meat 11/23/02 327 62.3 54.25596
10 10     3 139 4.383333 Meatsemidesert      2. Spring semidesert  
AksToKa Aksenger   KZP   Meat  4/10/03 100 47.3 40.49450
     bcs prcfat     cageyrs
1  2.75 26.533 -0.46162659
2  2.00 20.192 -0.07829326
3  2.50 24.478  0.23004008
4  2.50 24.414  0.45226230
5  2.50 24.490  3.03837341
6  1.75 18.337  3.42170674
7  2.25 22.403  3.73004008
8  3.25 31.087  3.95226230
9  2.50 24.318  1.03837341
10 2.00 20.368  1.42170675