I only installed the binary lme4 for the R2.12.0. After I remove the lme4, I
change to the R BLAS and reinstall the binary lme4. The results are always
true now.
Hope this information is useful.
Huang
On Thu, Oct 21, 2010 at 11:11 PM, Marc Schwartz <marc_schwa...@me.com>wrote:
>
> On Oct 21, 2010, at 8:47 AM, Federico Calboli wrote:
>
> > Mark,
> >>
> >> To the extent that it may be helpful here and I can do more if need be,
> I built 32 bit R 2.12.0 patched on Snow Leopard (10.6.4), using the R BLAS
> rather than Apple's veclib. This is on an early 2009 17" MBP with a 2.93 Ghz
> Core 2 Duo (MacBookPro5,2) and 4Gb of RAM.
> >>
> >> Based upon Doug's comment in this thread that the issue may be related
> to the use of Apple's veclib BLAS, as opposed to R's reference BLAS, I ran
> some tests.
> >>
> >> My config includes:
> >>
> >> --without-blas --without-lapack
> >>
> >> just to be sure that the above is the correct invocation, based upon
> what I found online.
> >>
> >> Using this build, with all CRAN packages freshly installed using this
> build, I ran the example used here with lme4 0.999375-35. I get:
> >>
> >> 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.))
> >> [1] TRUE
> >>
> >>
> >>
> >> I then created a function so that I could use replicate() to run this
> test a "larger" number of times:
> >>
> >> testlme4 <- function()
> >> {
> >> 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.))
> >> }
> >>
> >>
> >> RES <- replicate(1000, testlme4())
> >>
> >>> all(RES)
> >> [1] TRUE
> >>
> >>> table(RES)
> >> RES
> >> TRUE
> >> 1000
> >>
> >> Does the example need to be run a "very large" number of times to be
> sure that it does not fail, or is the above a reasonable indication that the
> use of R's BLAS is a more appropriate default option for R on OSX? If I am
> not mistaken (and somebody correct me if wrong), R's BLAS is the default on
> Windows and Linux (from my recollections on Fedora). Why should OSX be
> different in that regard?
> >
> > Thanks for the very informative post. I added R-Mac in my reply to see if
> someone can come up with a response to your query. It would also be
> interesting to know if it were possible to switch the OSX R binary to use
> the R BLAS library.
> >>
> >> Also, as an aside to Federico, I use 32 bit R on OSX largely because I
> have to interact with an Oracle server via RODBC. The only ODBC drivers
> available for Oracle on OSX are 32 bit and they are not compatible with 64
> bit R. It would be rather cumbersome when running reports (via Sweave) to
> first extract the data in 32 bit R and then switch to 64 bit R to run the
> reports. I can run it all in a single step using 32 bit R. I also do not
> have a need for the larger memory address space afforded by 64 bit R.
> >
> > I'm very primitive in any integration between R and anything else, so
> much so that I abandoned Emacs (well integrated with R) for Vim (not as well
> integrated). On the other hand I do need the greater memory address space of
> R64. I understand my needs and habits are not universally shared, but, if
> the *only* reason for using R32 vs R64 is the 20% speed difference, I'd use
> R64 for running lme4.
>
>
>
> OK, so here is some more data. I wondered if my build using R's BLAS may
> have possibly been a Type II error. So I re-built 32 bit R (same SVN
> checkout code) using:
>
> --with-blas='-framework vecLib' --with-lapack
>
> I then completely removed my old R build (using R's BLAS) and the installed
> CRAN packages. I re-installed R using the above configuration and then
> cleanly re-installed the required CRAN packages.
>
> Here are the results:
>
> 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.))
> [1] FALSE
>
> testlme4 <- function()
> {
> 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.))
> }
>
> RES <- replicate(1000, testlme4())
>
> > all(RES)
> [1] FALSE
>
> > table(RES)
> RES
> FALSE TRUE
> 496 504
>
> So the test in question seems to fail with P(Failure) ~ 0.5 using Apple's
> veclib BLAS.
>
> I should also note that all of my testing is from the CLI using the OSX
> terminal. I do not use R.app.
>
> In response to Huang's reply, regarding his use of the shared lib approach,
> I wonder if there is some other interaction going on, either in the BLAS
> libs, or perhaps in the installed version of lme4, when one BLAS versus the
> other is in use at the time of lme4 installation, since it is installed from
> source on OSX. Note that I used a fully clean build of R and the required
> CRAN packages for each set of tests.
>
> If there is some other testing that I can do, let me know. But the above
> results with a clean build of R and lme4 each time, would seem to further
> reinforce a reconsideration of the use of Apple's veclib BLAS as the default
> for CRAN binary builds of R on OSX.
>
> Regards,
>
> Marc
>
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>
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