Dear R mixed model Users and Dr. Bates,

I am trying to work out a problem given in Nested design - Design of Experiments by  Montgomery -  p.561 
using lme
It is a mixed model with Supplier as fixed effect and batches within the supplier as random effects.

When I tried my hands on lmer instead of lme, I get what is required  as below.
 
> proclme=lmer(Purity~Supplier+(1|Supplier:Batch),process)
> print(summary(proclme))
Linear mixed-effects model fit by REML 
Formula: Purity ~ Supplier + (1 | Supplier:Batch) 
   Data: process 
   AIC   BIC logLik MLdeviance REMLdeviance
 150.8 157.2 -71.42      146.9        142.8
Random effects:
 Groups         Name        Variance Std.Dev.
 Supplier:Batch (Intercept) 1.7162   1.3100   Here we have the variance of random effect exactly as in the book
 Residual                   2.6368   1.6238  
number of obs: 36, groups: Supplier:Batch, 12
 
Fixed effects:
            Estimate Std. Error t value
(Intercept)  -0.4167     0.8055 -0.5173
SupplierT2    0.7500     1.1391  0.6584
SupplierT3    1.5833     1.1391  1.3900
 
Correlation of Fixed Effects:
           (Intr) SpplT2
SupplierT2 -0.707       
SupplierT3 -0.707  0.500

But using lme, it is not working. Is there any way out.lme is not accepting the format random=~1|Supplier : Batch
I am more comfortable using lme since the supporting docs are exaustive and we have access to many support functions for lme

This code is not appropriate and also not working
library(nlme)
proclme=lme(Purity~Supplier,random = ~1|Supplier/Batch,process)
summary(proclme)
VarCorr(proclme)


Divaker

Dr. C. Divaker Durairaj, ME, Ph.D
Professor, Farm Machinery
Agricultural Machinery Research Centre
Tamil Nadu Agricultural University
Coimbatore 641003, India
Ph: 91-422-6611204

	[[alternative HTML version deleted]]


