Hi Andrea,

In  Chapter 19 of Galecki, Burzykowski  recent book titled "Linear
Mixed-Effects Models Using R: Step-by-Step Approach" you will find an
example of
using lme() function for  amode with crossed random effects.  Related
syntax (see below) is distributed with nlmeU package available from cran.
 Note that execution time can be fairly long.

Best,

Andrzej

PS.  Note the use of auxiliary variables named one1 and one2.

library(lattice)
data(fcat, package = "nlmeU")

## ---->>>> NOTE: Code used in Panels R19.1 - R19.7 is stored in Ch19mer.R
file


###################################################
### code chunk: R19.8
###################################################
library(nlme)

fcat1 <- within(fcat, one1 <- one2 <- 1L)
system.time(
   fm19.2 <-
      lme(scorec ~ 1,
          random = list(
          one1 = pdIdent(~target - 1),
          one2 = pdIdent(~id - 1)),
          data = fcat1))
fm19.2                                          # M19.2: (19.2)




On Tue, Nov 19, 2013 at 8:46 AM, Friso Muijsers <
Friso.muijsers@uni-oldenburg.de> wrote:

> Ok, I'm sorry for directing you into the wrong direction. Probably someone
> else has an idea?
>
> Am 11/19/2013 2:28 PM, schrieb Andrea Onofri:
>
>  Hello Friso,
>>
>> very many thanks for your answer. I think that the coding in
>> StatsExchange is equivalent to:
>>
>> lme(Y~ 1, random=~1|A/B, data=X, weights=varIdent(form=~1|A))
>>
>> which is actually different from what I am looking for.
>> Indeed:
>>
>> Y <- c(1.6, 2.3, 2.25, 3, 1.6, 2.35, 1.5, 2.85, 1.45, 2.65, 1.95,
>> 2.65, 1.1, 2.1, 0.7, 2.25, 1.15, 1.65, 0.8, 1.7, 0.95, 1.65,
>> 0.75, 1.35, 1, 2.05, 0.8, 2, 0.75, 1.9, 0.65, 1.9, 1.4, 2.1,
>> 1.6, 1.95, 1.05, 1.75, 0.85, 1.75, 1.3, 1.95, 0.95, 1.55, 1,
>> 1.1, 0.65, 1.05, 1.3, 1.45, 1.05, 0.9, 0.8, 0.9, 0.65, 0.7)
>> A <- structure(c(1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 4L,
>> 4L, 4L, 4L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 8L,
>> 8L, 8L, 8L, 9L, 9L, 9L, 9L, 10L, 10L, 10L, 10L, 11L, 11L, 11L,
>> 11L, 12L, 12L, 12L, 12L, 13L, 13L, 13L, 13L, 14L, 14L, 14L, 14L
>> ), .Label = c("A", "B", "C", "D", "E", "F", "G", "H", "I", "J",
>> "K", "L", "M", "N"), class = "factor")
>> B <- structure(c(1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L,
>> 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L,
>> 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L,
>> 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L), .Label = c("A",
>> "B", "C", "D"), class = "factor")
>>
>> mod <- lmer(Y ~ 1 + (1|A) + (1|B))
>>
>> Results in:
>>   ......
>> Linear mixed model fit by REML
>> Formula: Y ~ 1 + (1 | A) + (1 | B)
>> ....
>> Random effects:
>> Groups Name Variance Std.Dev.
>> A (Intercept) 0.180203 0.42450
>> B (Intercept) 0.163587 0.40446
>> Residual 0.088169 0.29693
>> Number of obs: 56, groups: A, 14; B, 4
>>
>> Fixed effects:
>> Estimate Std. Error t value
>> (Intercept) 1.4839 0.2352 6.308
>>
>> While:
>>
>> mod2 <- lme(Y ~ 1, random=list(A=~1, B=~1))
>>
>> Results in:
>>
>> Linear mixed-effects model fit by REML
>> .......
>> Random effects:
>> Formula: ~1 | A
>> (Intercept)
>> StdDev: 0.3732374
>>
>> Formula: ~1 | B %in% A
>> (Intercept) Residual
>> StdDev: 0.4641765 0.1905162
>>
>> Fixed effects: Y ~ 1
>> Value Std.Error DF t-value p-value
>> (Intercept) 1.483929 0.1201919 42 12.34633 0
>>
>> It looks quite different, but perhaps I am missing something? Thank
>> you again very much
>>
>> Andrea Onofri
>> Department of Agroenvironmental and Crop Sciences
>> University of Perugia
>> Italy
>>
>>
>> On 19 November 2013 10:48, Friso Muijsers
>> <Friso.muijsers@uni-oldenburg.de> wrote:
>>
>>> Am 11/19/2013 9:39 AM, schrieb andrea.onofri@unipg.it:
>>>
>>>> Dear all,
>>>>
>>>> I am trying to fit a simple model, relating to a randomised block
>>>> design where both blocks (A) and treatments (B) are random effects. Coding
>>>> in lmer, this model would be:
>>>>
>>>> model <- lmer(Y ~ 1 + (1|A) + (1|B))
>>>>
>>>> However, I would also like to be able to 'manipulate' the correlation
>>>> structure and thus I assume I have to revert to the lme function in the
>>>> nlme package. In other cases I have been able to fit non-nested effects in
>>>> lme by appropriately using the pdMat construct, but, after several efforts,
>>>> I do not seem to succeed in this simple case. I would greatly appreciate
>>>> any hints that puts me in the right direction. I thank you very much in
>>>> advance.
>>>>
>>>> Regards
>>>>
>>>> Andrea Onofri
>>>>
>>>> _______________________________________________
>>>> R-sig-mixed-models@r-project.org mailing list
>>>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>>>
>>> Hello,
>>>
>>> if I understand correctly, you want to specifiy multiple but non-nested
>>> random effects? If so, I recently found a post on statsExchange where
>>> someone has found a solution. I didn't check it though, but perhaps it
>>> fits your needs:
>>>
>>> |fit<-  lme(Y~  time,  random=list(year=~1,  date=~time),  data=X,
>>>  weights=varIdent(form=~1|year))
>>>
>>> or adapted to your general lmer example
>>>
>>> ||fit<-  lme(Y~  1,  random=list(A=~1,  B=~1),  data=X,
>>>  weights=varIdent(form=~1|A))||
>>>
>>> You can read the details here:
>>>
>>> http://stats.stackexchange.com/questions/58669/
>>> specifying-multiple-separate-random-effects-in-lme
>>>
>>> Perhaps that works (didn't check it myself, yet)
>>>
>>> Greetings
>>> |
>>>
>>> --
>>> Friso Muijsers
>>>
>>> Institute for Chemistry and Biology of the Marine Environment (ICBM)
>>> Carl-von-Ossietzky University Oldenburg
>>> Schleusenstrasse 1
>>> 26382 Wilhemshaven
>>>
>>>
>>>          [[alternative HTML version deleted]]
>>>
>>> _______________________________________________
>>> R-sig-mixed-models@r-project.org mailing list
>>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>>
>>
>
> --
> Friso Muijsers
>
> Institute for Chemistry and Biology of the Marine Environment (ICBM)
> Carl-von-Ossietzky University Oldenburg
> Schleusenstrasse 1
> 26382 Wilhemshaven
>
> _______________________________________________
> R-sig-mixed-models@r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>

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