lme - nested - One fixed effect and another within thatas random effect

What do you mean by not working? Your lme and lmer code seem to be
equivalent.
Not really.  Supplier:Batch is the batch grouping factor without the
implicit nesting.  That is, it has a different level for each
different batch (which seems to me to be the only sensible way to
define such a factor but, in the old days, people seemed to think it
was important to specify nesting implicitly).  The expression
Supplier/Batch implies two random effects, one for Supplier and one
for Supplier:Batch.

The simple way out is to define

process$realBatch <- with(process, Supplier:Batch)

and fit the lme model with random = ~ 1|realBatch
-----Original Message-----
From: r-sig-mixed-models-bounces at r-project.org
[mailto:r-sig-mixed-models-bounces at r-project.org] On Behalf Of Divaker
Sent: Monday, November 26, 2007 9:26 PM
To: r-sig-mixed-models at r-project.org
Subject: [R-sig-ME] lme - nested - One fixed effect and
another within thatas random effect

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

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lme - nested - One fixed effect and another within thatas random effect

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