From the help page:
'princomp' only handles so-called R-mode PCA, that is feature
extraction of variables. If a data matrix is supplied (possibly
via a formula) it is required that there are at least as many
units as variables. For Q-mode PCA use 'prcomp'.
On Wed, 24 Oct 2007, "Julia Kröpfl" wrote:
Hi there!
I have tried your idea with rotating the matrix and performing a normal PCA, but the
problem is, that "princomp" can only perform PCA if there are more rows than
columns. When I rotate the matrix, I get my observations put in the columns and my
features in the rows (more columns than rows) and therefore get an error message.
Any ideas what to do?
Thx for your help,
I really appreciate it!
Julia
-------- Original-Nachricht --------
Datum: Fri, 12 Oct 2007 23:38:01 +0300
Von: "Kenn Konstabel" <[EMAIL PROTECTED]>
An: "Julia Kröpfl" <[EMAIL PROTECTED]>
CC: r-help@r-project.org
Betreff: Re: [R] Q-type factor analysis
On 10/12/07, "Julia Kröpfl" <[EMAIL PROTECTED]> wrote:
Is there a package in R that does Q-type factor analysis?
I know how to do principal component analysis, but haven't found any
application of Q-type factor analysis.
Q-mode factor analysis is not a separate "type" of factor analysis but (in
old-fashioned psychological slang) analyzing of rows rather than the
columns
of data matrix. So you can transpose your data (with t() if it's a matrix)
and do an "ordinary" factor analysis or PCA.
Kenn
--
Brian D. Ripley, [EMAIL PROTECTED]
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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