prcomp: problem with zeros? (PR#8870)

Full_Name: Juha Heljoranta
Version: R 2.1.1 (2005-06-20)
Not a current version of R.
OS: Gentoo Linux
Submission from: (NULL) (88.112.29.250)

prcomp has a bug which causes following error

   Error in svd(x, nu = 0) : infinite or missing values in 'x'

on a valid data set (no Infs, no missing values). The error is most likely
caused by the zeros in data.
Why do you say that?  Without a reproducible example, we cannot judge what 
is going on.  If you called prcomp with scale=TRUE on a matrix that has a 
completely zero (or constant) column, then this is a reasonable error 
message.
My code and temporary workaround:

 m = matrix(...
 ...
 prcomp(m, center = TRUE, scale = TRUE)
 Error in svd(x, nu = 0) : infinite or missing values in 'x'

 m = matrix(...
 ...
 # ugly work around
 m = m + 1e-120
 # too small values will not work
 # m = m + 1e-150
 prcomp(m, center = TRUE, scale = TRUE)
 # success

The matrix in question is ~1024x13000 containing double values, thus totaling of
~103M of raw data. I can put it online if needed.

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Brian D. Ripley,                  ripley at stats.ox.ac.uk
Professor of Applied Statistics,  http://www.stats.ox.ac.uk/~ripley/
University of Oxford,             Tel:  +44 1865 272861 (self)
1 South Parks Road,                     +44 1865 272866 (PA)
Oxford OX1 3TG, UK                Fax:  +44 1865 272595

On Wed, 17 May 2006, juha.heljoranta at iki.fi wrote:
prcomp has a bug which causes following error

   Error in svd(x, nu = 0) : infinite or missing values in 'x'

on a valid data set (no Infs, no missing values). The error is most 
likely
caused by the zeros in data.
Why do you say that?  Without a reproducible example, we cannot judge 
what
is going on.  If you called prcomp with scale=TRUE on a matrix that 
has a
completely zero (or constant) column, then this is a reasonable error
message.
Constant columns (which is a likely reason here) indeed become NaN 
after scale(), but the error message was:

Error in svd(x, nu = 0) : infinite or missing values in 'x'

and calling this 'reasonable' is stretching the limits of reason.

However, in general this is "easy" to solve:  scale() before the 
analysis and replace NaN with 0 (prcomp handles zeros).  For instance,

x <- scale(x)
x[is.nan(x)] <- 0
prcomp(x)

(and a friendly prcomp() would do this internally.)

cheers, jari oksanen
--
Jari Oksanen, Oulu, Finland