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

 


	[[alternative HTML version deleted]]

