non-nested effects in lme

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

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


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