[R] Q-type factor analysis
Prof Brian Ripley
ripley at stats.ox.ac.uk
Wed Oct 24 10:46:33 CEST 2007
>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!
> -------- Original-Nachricht --------
>> Datum: Fri, 12 Oct 2007 23:38:01 +0300
>> Von: "Kenn Konstabel" <lebatsnok at gmail.com>
>> An: "Julia Kröpfl" <jkroepfl at gmx.net>
>> CC: r-help at r-project.org
>> Betreff: Re: [R] Q-type factor analysis
>> On 10/12/07, "Julia Kröpfl" <jkroepfl at gmx.net> 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
>> of data matrix. So you can transpose your data (with t() if it's a matrix)
>> and do an "ordinary" factor analysis or PCA.
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
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