I would like to impute missing data in a set of correlated
variables (columns of a matrix). It looks like transcan() from
Hmisc is roughly what I want. It says, transcan automatically
transforms continuous and categorical variables to have maximum
correlation with the best linear combination of
At the risk of stirring up a hornet's nest , I'd suggest that
means are dangerous in such applications. A nice paper
on combining ratings is: Gilbert Bassett and Joseph Persky,
Rating Skating, JASA, 1994, 1075-1079.
url:www.econ.uiuc.edu/~rogerRoger Koenker
email
On 11/30/04 11:23, roger koenker wrote:
At the risk of stirring up a hornet's nest , I'd suggest that
means are dangerous in such applications. A nice paper
on combining ratings is: Gilbert Bassett and Joseph Persky,
Rating Skating, JASA, 1994, 1075-1079.
Here is the abstract, which seems to
Jonathan Baron wrote:
I would like to impute missing data in a set of correlated
variables (columns of a matrix). It looks like transcan() from
Hmisc is roughly what I want. It says, transcan automatically
transforms continuous and categorical variables to have maximum
correlation with the best
On 11/30/04 13:21, Frank E Harrell Jr wrote:
Jonathan Baron wrote:
I would like to impute missing data in a set of correlated
variables (columns of a matrix). It looks like transcan() from
Hmisc is roughly what I want. It says, transcan automatically
transforms continuous and categorical
