Dear All,
Thank You for the quick responses.
Managed to solve my problem through:
http://www.faculty.biol.ttu.edu/strauss/multivar/R/SamplePCABootstrap.R.txt
or
http://r.789695.n4.nabble.com/bootstrapped-eigenvector-method-following-prcomp-td877655.html
Used the first one however, code is too long
psych does not currently have bootstrapped confidence intervals for loadings.
That is a reasonable request and I will try to add it, perhaps in the “real
soon now” version of 1.5.4 (almost finished), perhaps in the next release,
Bill
> On Apr 13, 2015, at 2:38 PM, stephen sefick wrote:
>
> H
Hi,
Please search the mailing list archives for this, or type bootstrapped PCA
R into google. Please provide a minimal self-contained example of what you
are trying to solve. Please read the posting guide that is referenced at
the end of every email.
kind regards,
Stephen
On Mon, Apr 13, 2015 at
Dear All,
I am relatively new in R.
Im working with the 'psych' package and 'principal' function.
I would like to know how to generate the bootstraped conf.intervals
for loadings,
looking for sth similar to setting 'n.iter' argument for the 'fa' function.
If in 'psych' can't work and suggest
Hi He Zhang,
>> Is the following right for extracting the scores?
>> ...
>> pca$loadings
>> pca$score
Yes.
But you should be aware that the function principal() in package psych is
standardizing your data internally, which you might not want. That is, the
analysis is being based on the correla
Hi,
I am also doing PCA.
Is the following right for extracting the scores?
library(psych)
pca<-principal(data,nfactors=,rotate="varimax",scores=T)
pca$loadings
pca$score
Best regards,
He
On Tue, Nov 30, 2010 at 10:22 AM, Liviu Andronic wrote:
> Dear all
> I'm unable to find an example of extra
Hi Liviu,
>> However, I'm still confused on how to compute the scores when rotations
>> (such as 'varimax' or other methods in GPArotation) are applied.
PCA does an orthogonal rotation of the coordinate system (axes) and further
rotation is not usually done (in contrast to factor analysis). Nei
Take 2 on this. Below I'm pasting the code to perform PCA in R
(without any rotation), manually; using ?princomp; and using
?principal. I also point out some differences in teh output and
terminology of the two functions. In short, I found how to compute the
scores of principal components when no r
Dear all
I'm unable to find an example of extracting the rotated scores of a
principal components analysis. I can do this easily for the un-rotated
version.
data(mtcars)
.PC <- princomp(~am+carb+cyl+disp+drat+gear+hp+mpg, cor=TRUE, data=mtcars)
unclass(loadings(.PC)) # component loadings
summary(
Hi everyone,
So I am trying to see which ecological parameter of different parks in nyc
influence the most the diversity of the medium-sized mammals in those parks.
I have a bunch of different parameters for each park I'm done studying and
the presence (1) and absence (0) of each mammal. I wanted
n my
samples are not in a correct order?
As you realize I am quite new with R. Thank you so much for taking your time
helping me, I really appreciate it.
Regards, Monna
> From: [EMAIL PROTECTED]
> To: [EMAIL PROTECTED]; r-help@r-project.org
> Subject: AW: [R] PCA analysis
> Date: T
Monna,
The way i do it is to re-create the biplot for the PCA . I am attaching my
code (i am sure this can be done even easier . but this works as well)
where i am using pca() function from labdsv and my data is called veg1.
library (labdsv)
pca.1<-pca(veg1,cor=TRUE)
# The scores are
-project.org
Betreff: [R] PCA analysis
Hi,
I have a problem with making PCA plots that are readable.
I would like to set different sympols instead of the numbers of my samples
or their names, that I get plotted (xlabs).
How is this possible? With points, i don4t seem to get the right data
Hi,
I have a problem with making PCA plots that are readable.
I would like to set different sympols instead of the numbers of my samples or
their names, that I get plotted (xlabs).
How is this possible? With points, i don´t seem to get the right data plotted
onto the PCA plot, as I do not qu
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