[R-sig-ME] non-nested effects in lme

[Friso Muijsers]

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 at uni-oldenburg.de> wrote:
>> Am 11/19/2013 9:39 AM, schrieb andrea.onofri at 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 at 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 at 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