Thank you Thomas and Uwe for this.
This is really odd, because today I can neither reproduce it myself (I
rebooted my computer this morning). I try to recall what I did
yesterday: I did _not_ reboot my machine, but I did install the latest
binaries of R 2.1.1 and 2.2.0dev from CRAN. I did close all R sessions
and restarted them by R --vanilla (only one at the time) and got the
same errors over and over for hours (trust me, I was really frustrated
with my analysis). Maybe a reboot would have solved it - I should know
better and try that first! A worse scenario is that the hardware was
overheated or starts to fall apart.
To answer you question Uwe, I don't know about the compiler settings
since I got the pre-build binaries from CRAN.
Best wishes
Henrik
Uwe Ligges wrote:
> Thomas Lumley wrote:
>
>> I can't reproduce this on R2.2.0dev on Windows XP (in a few hundred
>> tries), or running under Valgrind on AMD64 Linux (in four or five tries).
>
>
> Cannot reproduce either (using R-2.1.1 and an older version of R-devel,
> though). Maybe a compiler issue?
> Henrik, do you use exactly the compiler set up mentioned in the manuals?
> Which version of gcc? Did your emember to replace the f771.exe?
>
> Uwe
>
>
>
>> -thomas
>>
>>
>> On Fri, 26 Aug 2005, Henrik Bengtsson wrote:
>>
>>
>>> Hi,
>>>
>>> I've spotted a possible memory leakage/violation in the latest R v2.1.1
>>> patched and R v2.2.0dev on Windows XP Pro SP2 Eng.
>>>
>>> I first caught it deep down in a nested svd algorithm when subtracting a
>>> double 'c' from a integer vector 'a' where both had finite values but
>>> when assigning 'a <- a - c' would report NaNs whereas (a-c) alone would
>>> not. Different runs with the identical data would introduce NaNs at
>>> random positions, but not all the time.
>>>
>>> Troubleshooting is after a couple of hours still at v0.5, but here is a
>>> script that generates the strange behavior on the above R setups. I let
>>> the script speak for itself. Note that both the script 'strange.R' and
>>> the data 'strange.RData' is online too, see code below.
>>>
>>> People on other systems (but also on Windows), could you please try it
>>> and see if you can reproduce what I get.
>>>
>>> Cheers
>>>
>>> Henrik
>>>
>>>
>>> # The following was tested on: Windows XP Pro SP2 Eng with
>>> # i) R Version 2.1.1 Patched (2005-08-25)
>>> # ii) R 2.2.0 Under development (unstable) (2005-08-25 r35394M)
>>>
>>> # Start 'R --vanilla' and source() this script, i.e.
>>> # source("http://www.maths.lth.se/help/R/strange.R";)
>>> # If you do not get any errors, retry a few times.
>>>
>>> foo <- function(x) {
>>> print(list(
>>> name=as.character(substitute(x)),
>>> storage.mode=storage.mode(x),
>>> na=any(is.na(x)),
>>> nan=any(is.nan(x)),
>>> inf=any(is.infinite(x)),
>>> ok=all(is.finite(a))
>>> ))
>>> print(length(x))
>>> print(summary(x))
>>> }
>>>
>>> # Load data from a complicated "non-reproducible" algorithm.
>>> # The below errors occur also when data is not
>>> # saved and then reloaded from file. Data was generated in
>>> # R v2.1.1 patched (see above).
>>> if (file.exists("strange.RData")) {
>>> load("strange.RData")
>>> } else {
>>> load(url("http://www.maths.lth.se/help/R/strange.RData";))
>>> }
>>>
>>> # First glance at data...
>>> foo(a)
>>> foo(c)
>>>
>>> ## $name
>>> ## [1] "a"
>>> ##
>>> ## $storage.mode
>>> ## [1] "integer"
>>> ##
>>> ## $na
>>> ## [1] FALSE
>>> ##
>>> ## $nan
>>> ## [1] FALSE
>>> ##
>>> ## $inf
>>> ## [1] FALSE
>>> ##
>>> ## $ok
>>> ## [1] TRUE
>>> ##
>>> ## [1] 15000
>>> ## Min. 1st Qu. Median Mean 3rd Qu. Max.
>>> ## 41.0 51.0 63.0 292.2 111.0 65170.0
>>> ## $name
>>> ## [1] "c"
>>> ##
>>> ## $storage.mode
>>> ## [1] "double"
>>> ##
>>> ## $na
>>> ## [1] FALSE
>>> ##
>>> ## $nan
>>> ## [1] FALSE
>>> ##
>>> ## $inf
>>> ## [1] FALSE
>>> ##
>>> ## $ok
>>> ## [1] TRUE
>>> ##
>>> ## [1] 1
>>> ## Min. 1st Qu. Median Mean 3rd Qu. Max.
>>> ## 53.43 53.43 53.43 53.43 53.43 53.43
>>> ##
>>>
>>> # But, trying the following, will result in
>>> # no-reproducible error messages. Sometimes
>>> # it errors at kk==1, sometimes at kk >> 1.
>>> # Also, look at the different output for
>>> # different kk:s.
>>> for (kk in 1:100) {
>>> cat("kk=",kk, "\n")
>>> print(summary(a-c))
>>> }
>>>
>>> ## kk= 1
>>> ## Min. 1st Qu. Median Mean 3rd Qu.
>>> Max.
>>> ## -7.741e+307 -2.431e+00 9.569e+00 5.757e+01
>>> ## kk= 2
>>> ## Min. 1st Qu. Median Mean 3rd Qu. Max.
>>> ## -12.430 -2.431 9.569 238.700 57.570 65120.000
>>> ## kk= 3
>>> ## Min. 1st Qu. Median Mean 3rd Qu. Max.
>>> ## -12.430 -2.431 9.569 57.570 65120.000
>>> ## kk= 4
>>> ## Min. 1st Qu. Median Mean 3rd Qu. Max.
>>> ## -12.430 -2.431 9.569 238.700 57.570 65120.000
>>> ## kk= 5
>>> ## Min. 1st Qu. Median Mean 3rd Qu. Max.
>>> ## -12.430 -2.431 9.569 238.700 57.570 65120.000
>>> ## kk= 6
>>> ## Error in quantile.default(object) : missing values and NaN's
>>> ## not allowed if 'na.rm' is FALSE
>>>
>>>
>>> ## Comments: If you shorten down 'a', the bug occurs less frequently.
>>>
>>> ______________________________________________
>>> R-devel@r-project.org mailing list
>>> https://stat.ethz.ch/mailman/listinfo/r-devel
>>>
>>
>>
>> Thomas Lumley Assoc. Professor, Biostatistics
>> [EMAIL PROTECTED] University of Washington, Seattle
>>
>> ______________________________________________
>> R-devel@r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-devel
>
>
>
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