Mixed effects model where nested factor is not the repeated across treatments lme???

Hi Miki,
I just got the same problem with you couple hours ago.
Rusers (Anna, and Mark {thank you guys}) provide me a vary valuable  
information.
link to following address.

http://www.nabble.com/Tukey-HSD-(or-other-post-hoc-tests)-following-repeated-measures-ANOVA-td17508294.html#a17559307
for the A vs. B, A vs. C....
You could install and download the multcomp package and perform the  
post hoc test
such as
summary(glht(lmel,linfct=mcp(treatment="Tukey")))

hopefully it helps
Chunhao


Quoting M Ensbey <m.ensbey at unimelb.edu.au>:

Hi,



I have searched the archives and can't quite confirm the answer to this.
I appreciate your time...



I have 4 treatments (fixed) and I would like to know if there is a
significant difference in metal volume (metal) between the treatments.
The experiment has 5 blocks (random) in each treatment and no block is
repeated across treatments. Within each plot there are varying numbers
of replicates (random) (some plots have 4 individuals in them some have
14 and a range in between). NOTE the plots in one treatment are not
replicated in the others.



So I end up with a data.frame with 4 treatments repeated down one column
(treatment=A, B, C, D), 20 plots repeated down the next (block= 1 to 20)
and records for metal volume (metal- 124 of these)

I have made treatment and block a factor. But haven't grouped them (do I
need to and how if so)



The main question is in 3 parts:



1.	is this the correct formula to use for this situation:
lme1<-lme(metal~treatment,data=data,random=~1|block) (or is lme even the
right thing to use here?)



I get:

summary(lme1)

Linear mixed-effects model fit by REML

 Data: data

       AIC      BIC    logLik

  365.8327 382.5576 -176.9163



Random effects:

 Formula: ~1 | block

        (Intercept)  Residual

StdDev:   0.4306096 0.9450976



Fixed effects: Cu ~ Treatment

                 Value Std.Error  DF   t-value p-value

(Intercept)   5.587839 0.2632831 104 21.223688  0.0000 ***

TreatmentB -0.970384 0.3729675  16 -2.601792  0.0193 ***

TreatmentC  -1.449250 0.3656351  16 -3.963651  0.0011 ***

TreatmentD  -1.319564 0.3633837  16 -3.631323  0.0022 ***

 Correlation:

             (Intr) TrtmAN TrtmCH

TreatmentB -0.706

TreatmentC  -0.720  0.508

TreatmentD  -0.725  0.511  0.522



Standardized Within-Group Residuals:

        Min          Q1         Med          Q3         Max

-2.85762206 -0.68568460 -0.09004478  0.56237152  3.20650288



Number of Observations: 124

Number of Groups: 20



2.	if so how can I get p values for comparisons between every
group... ie is A different from B, is A different from C, is A different
from D, is B different from C, is B different from D etc... is there a
way to get all of these instead of just "is A different from B, is A
different from C, is A different from D" which summary seems to give?
3.	last of all what is the best way to print out all the residuals
for lme... I can get qqplot(lme1) is there a pre-programmed call for
multiple diagnostic plots like in some other functions...





Thankyou so Much for your time....



It is much appreciated

;-)



Miki




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