-- 
I have searched the archives and while there are several threads re:
repeated msrs anovas, I do not believe any pertain to my question precisely,
but please pardon me (and point me in the right direction) if this has
already been hashed out on the list.

The data:
Abundance data (*abund*) of one species on three treatments (*trt*) (four
plots each-*plot*) over thirty years (*year*).

I have tried several versions of running a rep msrs anova in R, such as
this:
*rmaov<-aov(abund~factor(trt)*factor(year)+Error(factor(plot)))

which yields the following:
Error: factor(plot)
                  Df  Sum Sq   Mean Sq   F value  Pr(>F)
factor(trt)     2   3779431   1889716  4.2387   0.05046 .
Residuals    9 4012394  445822
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Error: Within
                               Df   Sum Sq Mean Sq F value    Pr(>F)
factor(year)              24 22589670  941236 17.0362    < 2.2e-16 ***
factor(trt):factor(year)  48  5676184  118254  2.1404   0.0001193 ***
Residuals                216 11933837   55249  *


I installed the package DTK to try to run some post hoc analyses, but it
appears that this package can only do posthoc tests for one factor anovas,
whereas I'd like to consider trt, year, and their interaction.

For example:

*tk<-TK.test(abund, trt)
tk
  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = x ~ f)

$f
      diff       lwr      upr     p adj
D-C  59.90 -68.62717 188.4272 0.5161870
R-C 262.33 133.80283 390.8572 0.0000072
R-D 202.43  73.90283 330.9572 0.0007219*

This yields the comparisons between the treatments (C,D & R) only.

And with other methods, such as:
*lme(abund~ trt*year, random=~1|plot/trt)
*
It seems to only consider two of the three treatments (R&D--why isn't C
considered?):

*Linear mixed-effects model fit by REML
 Data: Erod_aov
       AIC      BIC    logLik
  4298.306 4331.459 -2140.153

Random effects:
 Formula: ~1 | plot
        (Intercept)
StdDev:    82.85344

 Formula: ~1 | trt %in% plot
        (Intercept) Residual
StdDev:     82.8531 320.2939

Fixed effects: abund ~ trt * year
                Value Std.Error  DF   t-value p-value
(Intercept) -37533.75  7551.046 285 -4.970670  0.0000
trtD         -4545.05 10678.792   9 -0.425614  0.6804
trtR        -15800.10 10678.792   9 -1.479578  0.1731
year            18.91     3.791 285  4.988331  0.0000
trtD:year        2.31     5.361 285  0.431240  0.6666
trtR:year        8.06     5.361 285  1.504202  0.1336
 Correlation:
          (Intr) trtD   trtR   year   trtD:y
trtD      -0.707
trtR      -0.707  0.500
year      -1.000  0.707  0.707
trtD:year  0.707 -1.000 -0.500 -0.707
trtR:year  0.707 -0.500 -1.000 -0.707  0.500

Standardized Within-Group Residuals:
       Min         Q1        Med         Q3        Max
-1.9015983 -0.5112724 -0.1167855  0.2227194  9.2301428

Number of Observations: 300
Number of Groups:
         plot trt %in% plot
           12            12*

I am obviously not grasping something simple here.  I'd appreciate any
thoughts you might have.

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