compute variance of every column in a matrix without a loop

Francisco J Molina <FJMolina at lbl.gov> writes:

Is it possible to compute the variance of every column in a matrix
without a loop?

apply(m,2,var)

O__  ---- Peter Dalgaard             Blegdamsvej 3  
  c/ /'_ --- Dept. of Biostatistics     2200 Cph. N   
 (*) \(*) -- University of Copenhagen   Denmark      Ph: (+45) 35327918
~~~~~~~~~~ - (p.dalgaard at biostat.ku.dk)             FAX: (+45) 35327907
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Is it possible to compute the variance of every column in a matrix
without a loop?

What about
 apply(X, 2, var)

Uwe Ligges
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Is it possible to compute the variance of every column in a matrix
without a loop?

If the matrix is m1,

apply(m1,2,var)

or, with missing data,

apply(m1,2,var,na.rm=T)

In general, apply() and related functions are well worth
understanding.

Jonathan Baron, Professor of Psychology, University of Pennsylvania
Home page: http://www.sas.upenn.edu/~baron
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Is it possible to compute the variance of every column in a matrix
without a loop?

No, but you can make it look as if there's no loop.

You can hide the for() loop inside a function with
  apply(m, 2, var)
if the reason you want to avoid a loop is to make your code look simpler.

In 1.5.0 you will be able to do
   colMeans(m*m)-colMeans(m)^2
if the reason is to have the loop take place in compiled code and be
faster.


	-thomas


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On Sat, 16 Mar 2002, Francisco J Molina wrote:

Is it possible to compute the variance of every column in a matrix
without a loop?

No, but you can make it look as if there's no loop.

You can hide the for() loop inside a function with
  apply(m, 2, var)
if the reason you want to avoid a loop is to make your code look simpler.

In 1.5.0 you will be able to do
   colMeans(m*m)-colMeans(m)^2

..., but only for well conditioned problems, because using Steiner's
theorem is risky from the computational point of view. Try this extreme
example several times:

  m <- rnorm(10, 1e8, 1e-7) # large mean, small variance
  mean(m^2) - mean(m)^2     # typical results: -4, -2, 0, 2, 4
  var(m)                    # reasonable, about 1e-14

BTW: Of course this is a computational problem that is *not* related to
R!

if the reason is to have the loop take place in compiled code and be
faster.

Uwe Ligges
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