[R] Q-type factor analysis

[Prof Brian Ripley]

>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" <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
>> 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,                  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)
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