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

From: M Ensbey <m.ensbey_at_unimelb.edu.au>
Date: Wed, 30 Jul 2008 21:03:53 +1000


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 ***


             (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



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