Dear Newmiller,
Thank you for your reply. I’ve just posted the same question in the
another forum for stats as you suggested.
Meanwhile, I would like to keep the question submitted to learn from R
users, if it is available .
Park
On Sun, May 9, 2021 at 6:01 AM Jeff Newmiller
wrote:
> This no
I am an author of the paper behind the fad package. I suspect that the call is
not correct. Actually, fad does not quite account for time series or other
structured data and you have to enter it, as in all general EFA packages as a n
x p matrix, with n the number of observations and p the number
This not being a question about R, but rather about statistics, or possibly
about a contributed package, means (per the Posting Guide) that you should be
asking in a statistics forum like stats.stackexchange.com or corresponding with
the author of the package in question. If you are lucky someon
Thank you for your reply. I am grouping citation and some social media
indictors ( number of tweets, mendeley readers, etc). The number of
citaions a paper recievs or the number of social media indicators that a
papers receives depends on time. For example a paper published in 2009 has
more time to
That step is easy, but context is hard. You really need to provide a
reproducible example. There are many models, many analysis tools, and many
timescales to choose from. In fact, this could easily be mistaken for a
question about statistics (not really on-topic here) since you have failed to
ey man!!
you need to change the format of your data as matrix
data <- as.matrix(correlation_data)
solucion <- factanal(covmat = data, factors=2)
that´s all!!!
enjoy
Rafael Ch.
--
View this message in context:
http://r.789695.n4.nabble.com/Factor-Analysis-Inputting-Correlation-Matrix-tp359
Varimax is orthogonal, promax is oblique. Varimax is generally not
recommended. See: Preacher, K. J., & MacCallum, R. C. (2003).
Repairing Tom Swift's electric factor analysis machine. Understanding
Statistics, 2(1), 13-43. (Google the title and you'll find a PDF).
The fa() function in the psy
At 6:19 AM -0700 6/14/11, Jay wrote:
Hi,
are there readily available R packages that are able to perform FA on
ordinal and/or nominal data?
If not, what other approaches and helpful packages would you suggest?
If by ordinal and nominal you mean just a few categories (e.g., a
mood scale or per
Hi Matt,
Did you try reading the documentation for factanal()? You can pull it
up by typing: help("factanal")
These give basically identical results using the raw data, the
covariance matrix, and the correlation matrix.
factanal(x = mtcars, factors = 3)
factanal(factors = 3, covmat = cov(mtcars
On Apr 19, 2011, at 13:58 , Robert Ruser wrote:
> Dear R Users,
> I'm wondering how is it possible to get |factor loadings| <1 in factor
> analysis model (factanal) performing maximum-likelihood estimation. Is
> it caused by some constraints? If so, what kind of constraints are
> placed and when
Jane,
>> Does someone know how to do fa and cfa with strong skewed data?
Your best option might be to use a robustly estimated covariance matrix as
input (see packages robust/robustbase).
Or you could turn to packages FAiR or lavaan (maybe also OpenMx). Or you
could try soft modelling via packa
On Sat, Nov 13, 2010 at 2:07 PM, Brima wrote:
>
> Thanks very much. However, I got an error message when I tried.
> What I did is that I created a correlation matrix named dat which is the
> only data I have and tried using the below
>
>> fa<- factanal(covmat = dat, factors=2, rotation="none", sco
Thanks very much. However, I got an error message when I tried.
What I did is that I created a correlation matrix named dat which is the
only data I have and tried using the below
> fa<- factanal(covmat = dat, factors=2, rotation="none", scores="none")
Error in factanal(covmat = dat, factors = 2,
Hello
On Sat, Nov 13, 2010 at 7:57 AM, Brima wrote:
>
> Hi all,
>
> This could be very basic. I want to do exploratory factor analysis but I
> don't have the data, rather I have the correlation matrix. How do I do this
> with just the correlation matrix? I know for principal components, I can
> j
See ?factanal, and ?ability.cov for a worked example.
On Fri, 12 Nov 2010, Brima wrote:
Hi all,
This could be very basic. I want to do exploratory factor analysis but I
don't have the data, rather I have the correlation matrix. How do I do this
with just the correlation matrix? I know for pri
On Tue, 15 Sep 2009, Moumita Das wrote:
Hi All,
There were lot of diffrences in the R and SPSS results for Exploratory
Factor Analysis.why is it so ?I used standard factor analysis functions
like:--
factanal(m, factors=3, rotation="varimax")
princomp(m, cor = FALSE, scores = TRUE, subset = rep(
On Sat, 2009-08-08 at 17:38 +0530, Arup Pramanik wrote:
> hi,
>
> Thanks for your reply but now it is giving me a message that Error in
> factanal(~., data = sony_factor, factors = 10, na.action = na.omit) :
> factor analysis applies only to numerical variables. All the
> variable which I am ha
On Fri, 2009-08-07 at 19:07 -0700, Arup wrote:
> Hi I am trying to run Factor Analysis using R...I am using the syntax
> factanal(m1, factors=3) but it's giving me an message Error in cov.wt(z) :
> 'x' must contain finite values only
> ...I am using a data set which is having only numeric variabl
At 8:17 AM -0400 3/31/09, John Fox wrote:
Dear TY,
Considering that you used different methods -- maximum-likelihood factor
analysis in R and principal components analysis in SAS -- the results are
quite similar (although the three rotated factors/components come out in
different orders).
I hop
As it was already pointed out by others, you used different methods
(principal components in SAS vs. factor analysis in R). When you use the
same method (+ varimax rotation) in both programs, there may still be a
*small* difference: this comes from (possibly) different stopping criteria.
In R, the
Dear TY,
Considering that you used different methods -- maximum-likelihood factor
analysis in R and principal components analysis in SAS -- the results are
quite similar (although the three rotated factors/components come out in
different orders).
I hope this helps,
John
> -Original Message
-BEGIN PGP SIGNED MESSAGE-
Hash: SHA1
Hi,
> I ran factor analysis using R and SAS. However, I had different outputs from
> R and SAS.
> Why they provide different outputs? Especially, the factor loadings are
> different.
> I did real dataset(n=264), however, I had an extremely different f
Dear Ricardo,
Factor scores are linear combinations of the original variables and
therefore to get factor scores, factanal() needs the data, not just the
correlation matrix among the variables.
Perhaps what you want is the factor-score coefficient matrix. Since you're
apparently using a varimax
Dear Kathleen,
You can use the sem() function in the sem package to fit CFA models from
tetrachoric correlations computed with the polycor package. See ?boot.sem in
the sem package for an example using polychoric correlations for ordinal
observed variables (tetrachoric correlations are a special c
you may check the ltm package:
http://wiki.r-project.org/rwiki/doku.php?id=packages:cran:ltm
I hope it helps.
Best,
Dimitris
Dimitris Rizopoulos
Ph.D. Student
Biostatistical Centre
School of Public Health
Catholic University of Leuven
Address: Kapucijnenvoer 35, Leuven, Belgium
Tel: +32/(
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