Hi,
I had a look at the MASS4 scripts in the MASS package, in Ch 11.3 Factor
Analysis, there is a section of codes like:
data(ability.cov)
ability.FA <- factanal(covmat = ability.cov, factors = 1)
ability.FA
(ability.FA <- update(ability.FA, factors = 2))
#summary(ability.FA)
round(loadings(ability.FA) %*% t(loadings(ability.FA)) +
diag(ability.FA$uniq), 3)
Unfortunately I still haven't received the book I ordered, so I can't look
this up.
Two questions:
1) What does the update() do? I mean, what happens if I replace it with
factanal(covmat = ability.cov, factors = 2)
2) What does the last command, the formulae in round() mean? I tried it
and it produced a matrix that looks kind of like correlation matrix of
some sort...
Cheers,
Kevin
------------------------------------------------------------------------------
Ko-Kang Kevin Wang
Postgraduate PGDipSci Student
Department of Statistics
University of Auckland
New Zealand
Homepage: http://www.stat.auckland.ac.nz/~kwan022
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Factor Analysis in MASS4
4 messages · Ko-Kang Kevin Wang, Brian Ripley, John Fox
On Thu, 29 Aug 2002, Ko-Kang Kevin Wang wrote:
Hi,
I had a look at the MASS4 scripts in the MASS package, in Ch 11.3 Factor
Analysis, there is a section of codes like:
data(ability.cov)
ability.FA <- factanal(covmat = ability.cov, factors = 1)
ability.FA
(ability.FA <- update(ability.FA, factors = 2))
#summary(ability.FA)
round(loadings(ability.FA) %*% t(loadings(ability.FA)) +
diag(ability.FA$uniq), 3)
Unfortunately I still haven't received the book I ordered, so I can't look
this up.
Two questions:
1) What does the update() do? I mean, what happens if I replace it with
factanal(covmat = ability.cov, factors = 2)
That's what it does. update always recalls the original call with the changes as given.
2) What does the last command, the formulae in round() mean? I tried it and it produced a matrix that looks kind of like correlation matrix of some sort...
It is the fitted correlations.
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 272860 (secr) Oxford OX1 3TG, UK Fax: +44 1865 272595 -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the subject !) To: r-help-request at stat.math.ethz.ch _._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._
I see. Just out of interest, is it possible to do a regresion analysis on the factors obtained from the factor analysis?
On Thu, 29 Aug 2002 ripley at stats.ox.ac.uk wrote:
Date: Thu, 29 Aug 2002 11:38:08 +0100 (BST) From: ripley at stats.ox.ac.uk To: Ko-Kang Kevin Wang <kwan022 at stat.auckland.ac.nz> Cc: R Help <r-help at stat.math.ethz.ch> Subject: Re: [R] Factor Analysis in MASS4 On Thu, 29 Aug 2002, Ko-Kang Kevin Wang wrote:
Hi,
I had a look at the MASS4 scripts in the MASS package, in Ch 11.3 Factor
Analysis, there is a section of codes like:
data(ability.cov)
ability.FA <- factanal(covmat = ability.cov, factors = 1)
ability.FA
(ability.FA <- update(ability.FA, factors = 2))
#summary(ability.FA)
round(loadings(ability.FA) %*% t(loadings(ability.FA)) +
diag(ability.FA$uniq), 3)
Unfortunately I still haven't received the book I ordered, so I can't look
this up.
Two questions:
1) What does the update() do? I mean, what happens if I replace it with
factanal(covmat = ability.cov, factors = 2)
That's what it does. update always recalls the original call with the changes as given.
2) What does the last command, the formulae in round() mean? I tried it and it produced a matrix that looks kind of like correlation matrix of some sort...
It is the fitted correlations. -- 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 272860 (secr) Oxford OX1 3TG, UK Fax: +44 1865 272595
Cheers, Kevin ------------------------------------------------------------------------------ Ko-Kang Kevin Wang Postgraduate PGDipSci Student Department of Statistics University of Auckland New Zealand Homepage: http://www.stat.auckland.ac.nz/~kwan022 -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the subject !) To: r-help-request at stat.math.ethz.ch _._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._
Dear Kevin,
At 10:03 PM 8/30/2002 +1200, Ko-Kang Kevin Wang wrote:
I see. Just out of interest, is it possible to do a regresion analysis on the factors obtained from the factor analysis?
Dear Kevin, Factor scores are variables, so, as a mechanical matter, you can use them in a subsequent analysis. (You won't get factor scores if you start with a covariance matrix, as opposed to a data matrix.) Factor scores can have large measurement-error components, however, which causes problems if you use them as explanatory variables in a regression. If your goal is to do a regression using the factors, and you can specify in advance which variables load on which factors, you might consider estimating the factor loadings and regression coefficients simultaneously, e.g., with the sem (structural-equation model) function in the sem package. John ----------------------------------------------------- John Fox Department of Sociology McMaster University Hamilton, Ontario, Canada L8S 4M4 email: jfox at mcmaster.ca phone: 905-525-9140x23604 web: www.socsci.mcmaster.ca/jfox ----------------------------------------------------- -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the subject !) To: r-help-request at stat.math.ethz.ch _._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._