What seems a more serious error is that the current code (and Peter's
modification) returns correlations computed from unordered factors,
and there are examples in packages 'agsemisc', 'ggm' and 'mi'.
And in all cases these are Pearson correlations, as is the use of
ordered factors in 'sfsmisc'.
The as.vector() seems to have been introduced to combat PR#7116, but
it is not the right fix as swapping the 'x' and 'y' arguments in the
regression example for that still crashed. (It seems to me that the
correct C-level fix is to check the length of the dimnames before
trying to access the second element.)
It would be tricky to do the coercion right for ordered factors (or
more general rankable classes): cor() accepts a data frame and does
as.matrix() on it: if the data frame includes such columns the
coercion has to be done column-by-column. So I decided to pass the
responsibility back to the caller, and only accept numeric arguments
(as the help page says). However, package 'mice' passes a logical
matrix, and as we do usually silently promote logical to numeric I
have continued to allow that.
Experience suggests that we have been too generous in doing autmatic
coercion in the past. It seems every time we tighten something up we
find a handful of packages that got dubious results from inappropriate
conversions.
On Mon, 8 Feb 2010, Prof Brian Ripley wrote:
On Mon, 8 Feb 2010, Peter Dalgaard wrote:
m...@biostat.mgh.harvard.edu wrote:
Full_Name: Marek Ancukiewicz
Version: 2.10.1
OS: Linux
Submission from: (NULL) (74.0.49.2)
Both cor() and cor.test() incorrectly handle ordered variables with
method="kendall", cor() incorrectly handles ordered variables for
method="spearman" (method="person" always works correctly, while
method="spearman" works for cor.test, but not for cor()).
In erroneous calculations these functions ignore the inherent ordering
of the ordered variable (e.g., '9'<'10'<'11') and instead seem to assume
an alphabetic ordering ('10'<'11'<'9').
Strictly speaking, not a bug, since the documentation has
x: a numeric vector, matrix or data frame.
respectively
x, y: numeric vectors of data values. ‘x’ and ‘y’ must have the
same length.
so noone ever claimed that class "ordered" variables should work.
However, the root cause is that as.vector on a factor variable (ordered
or not) converts it to a character vector, hence
rank(as.vector(as.ordered(9:11)))
[1] 3 1 2
Looks like a simple fix would be to use as.vector(x, "numeric") inside
the definition of cor().
A fix for that particular case: the problem is that relies on the underlying
representation. I think a better fix would be to do either of
- test for numeric and throw an error otherwise, or
- use xtfrm, which has the advantage of being more general and
allowing methods to be written (S3 or S4 methods in R-devel).
cor(9:11,1:3,method="k")
[1] 1
cor(as.ordered(9:11),1:3,method="k")
[1] -0.3333333
cor.test(as.ordered(9:11),1:3,method="k")
Kendall's rank correlation tau
data: as.ordered(9:11) and 1:3
T = 1, p-value = 1
alternative hypothesis: true tau is not equal to 0
sample estimates:
tau
-0.3333333
cor(9:11,1:3,method="s")
[1] 1
cor(as.ordered(9:11),1:3,method="s")
[1] -0.5
cor.test(as.ordered(9:11),1:3,method="s")
Spearman's rank correlation rho
data: as.ordered(9:11) and 1:3
S = 0, p-value = 0.3333
alternative hypothesis: true rho is not equal to 0
sample estimates:
rho
1
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c/ /'_ --- Dept. of Biostatistics PO Box 2099, 1014 Cph. K
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Brian D. Ripley, rip...@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
--
Brian D. Ripley, rip...@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
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