[R-sig-ME] Too big for lmer?

ONKELINX, Thierry Thierry.ONKELINX at inbo.be
Fri Aug 7 10:32:16 CEST 2009

Dear Kevin,

This is probably due to the huge number of levels in L which you put in
the fixed effects. lmer() calculates the correlation between all fixed
effects. That requires a huge matrix in this case.

Since you have far less random effects than fixed effects, it seems to
me more appropriate to interchange them. 

m0 <- lmer(y~H + (1|L), data = dat)

Anonther option it to use them both as crossed random effects.

m1 <- lmer(y~(1|H)+(1|L), data=dat)



ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature
and Forest
Cel biometrie, methodologie en kwaliteitszorg / Section biometrics,
methodology and quality assurance
Gaverstraat 4
9500 Geraardsbergen
tel. + 32 54/436 185
Thierry.Onkelinx at inbo.be

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than asking him to perform a post-mortem examination: he may be able to
say what the experiment died of.
~ Sir Ronald Aylmer Fisher

The plural of anecdote is not data.
~ Roger Brinner

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ensure that a reasonable answer can be extracted from a given body of
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-----Oorspronkelijk bericht-----
Van: r-sig-mixed-models-bounces at r-project.org
[mailto:r-sig-mixed-models-bounces at r-project.org] Namens Kevin W
Verzonden: donderdag 6 augustus 2009 20:57
Aan: r-sig-mixed-models at r-project.org
Onderwerp: [R-sig-ME] Too big for lmer?

I have a simple model that appears to be too big for lmer (using 2GB of
memory).  I _can_ fit the model with asreml, but I would like to make a
comparison with lmer. Simulated data is used below, but I have real data
this causing the same problem.

dat <- data.frame(H=sample(1:51, 24000, replace=TRUE),
                  L=sample(1:6101, 24000, replace=TRUE)) Heff <-
rnorm(51, sd=sqrt(40)) Leff <- rnorm(6101, sd=sqrt(1200)) err <-
rnorm(24000, sd=10) dat$y <- 100+Heff[dat$H] + Leff[dat$L] + err dat <-
transform(dat, H=factor(H), L=factor(L))
bwplot(y~H, dat)  # Looks right

Using asreml recovers the variance components almost exactly:

m1 <- asreml(y~1, data=dat, sparse=~L, random=~H)

           component std.error   z.ratio constraint
H           50.96058 10.249266  4.972121   Positive
R!variance 100.07324  1.056039 94.762853   Positive

Now try lmer:

m0 <- lmer(y~1+L+(1|H), data=dat)

Error: cannot allocate vector of size 1.1 Gb In addition: Warning
1: In model.matrix.default(mt, mf, contrasts) :
  Reached total allocation of 1535Mb: see help(memory.size)

Am I pushing lmer past its limits (given the 2GB of memory) or is there
a way to make this fit?

Kevin Wright

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