Hi All,

I've been searching the help archives but haven't found a workable  
solution to this problem.

I'm running an lme model with the following call:

 >lme.fnl <- lme(Max ~ S + Tr + Yr + Tr:Yr, random = ~1 |TID)
 > anova(lme.fnl)
             numDF denDF   F-value p-value
(Intercept)     1   168 19255.389  <.0001
S               1   168     5.912  0.0161
Tr              2   116    15.919  <.0001
Yr              1   168    77.837  <.0001
Tr:Yr           2   168    47.584  <.0001


 >summary(lme.fnl)
Linear mixed-effects model fit by REML
Data: NULL
        AIC      BIC    logLik
   580.6991 613.5399 -281.3496

Random effects:
Formula: ~1 | TID
         (Intercept)  Residual
StdDev:   0.3697006 0.5316062

Fixed effects: Max ~ S + Tr + Yr + Tr:Yr
                 Value Std.Error  DF   t-value p-value
(Intercept)  -13.5681  113.2623 168 -0.119793  0.9048
SM             0.2187    0.0957 168  2.284605  0.0236
TrT97       1375.5897  164.0060 116  8.387434  0.0000
TrT98       2890.9462  455.3497 116  6.348848  0.0000
Yr             0.0099    0.0567 168  0.174005  0.8621
TrT97:Yr      -0.6883    0.0821 168 -8.384798  0.0000
TrT98:Yr      -1.4463    0.2279 168 -6.347310  0.0000
Correlation:
          (Intr) SM     TrT97  TrT98  Yr     TT97:Y
SM        0.067
TrT97    -0.691 -0.049
TrT98    -0.248 -0.001  0.171
Yr       -1.000 -0.067  0.691  0.248
TrT97:Yr  0.691  0.048 -1.000 -0.171 -0.691
TrT98:Yr  0.248  0.001 -0.171 -1.000 -0.248  0.171

Standardized Within-Group Residuals:
         Min          Q1         Med          Q3         Max
-2.19017911 -0.58108001 -0.04983642  0.57323031  2.39811353

Number of Observations: 291
Number of Groups: 119



I'm specifically interested in differences of  in the differences  
between my treatment groups (3) and Year (Yr), and importantly in the  
interaction. Normally I'm used to running independent contrast  
analysis to explore these differences, but I'm not sure how to  
extract this information using lme. Can anyone point me in the right  
direction?

Thanks

Ken






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