For your codes, R32 is about 20% faster on my machine and as Simon Urbanek
suggested, R32 is more well tested. Though I forgot when he mentioned this
(Maybe a bit outdated).

I don't know whether there are recently information about the 32bit vs
64bit.

Huang

On Thu, Oct 21, 2010 at 12:30 AM, Federico Calboli <f.calboli@imperial.ac.uk
> wrote:

> On 20 Oct 2010, at 16:29, Douglas Bates wrote:
>
> > On Wed, Oct 20, 2010 at 10:18 AM, huang min <minhuangr@gmail.com> wrote:
> >> Hi, Prof. Bates,
> >>
> >> I tried some simple codes from Prof. Ripley.
> >>
> >> library(lme4)
> >> y <- (1:20)*pi; x <- (1:20)^2;group <- gl(2,10)
> >> M2. <- lmer (y ~ 1 + x + (1 + x | group))
> >> M2 <- lmer (y ~ x + ( x | group))
> >> identical(fixef(M2), fixef(M2.))
> >
> > This is exactly what is so frustrating.  By the time the model
> > matrices have been constructed M2. and M2 are exactly the same model
> > so how they end up with different answers is somewhat mysterious and
> > something that we can't reproduce on other systems.
>
>
> FWIW:
>
> R version 2.12.0 (2010-10-15)
> Copyright (C) 2010 The R Foundation for Statistical Computing
> ISBN 3-900051-07-0
> Platform: x86_64-apple-darwin9.8.0/x86_64 (64-bit)
>  ....
> [R.app GUI 1.35 (5632) x86_64-apple-darwin9.8.0]
>
>  library(lme4)
>  test = rep(0, 10000)
>  for (i in 1:10000){
>  y <- (1:20)*pi; x <- (1:20)^2;group <- gl(2,10)
>  M2. <- lmer (y ~ 1 + x + (1 + x | group))
>  M2 <- lmer (y ~ x + ( x | group))
>  test[i] = identical(fixef(M2), fixef(M2.))
>  }
>  sum(test)
> [1] 10000
>  sum(test == F)
> [1] 0
>
> I just fail to see why there is any need to use R32 and not R64, but that's
> just me.
>
> F
>
>
>
> --
> Federico C. F. Calboli
> Department of Epidemiology and Biostatistics
> Imperial College, St. Mary's Campus
> Norfolk Place, London W2 1PG
>
> Tel +44 (0)20 75941602   Fax +44 (0)20 75943193
>
> f.calboli [.a.t] imperial.ac.uk
> f.calboli [.a.t] gmail.com
>
>
>
>
>
>
>

	[[alternative HTML version deleted]]


