That's what I was looking for, thank you very much!
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R
Dear Orvalo Augusto,
If you are using a PC, you need to get the zip file, not the tar.gz file.
It is at http://personality-project.org/r/src/contrib/
For Macs it is an “other repository” (source option) at
http://personality-project.org/r/
Bill
On Jun 21, 2014, at 12:36 AM, Orvalho A
Dear!
I get this error when I try to install it on my linux PC:
root@orvaquimcism:~# R CMD INSTALL
/mnt/disco/downloads/R/psych_1.4.6.20.tar.gz
Error in rawToChar(block[seq_len(ns)]) :
embedded nul in string:
'\037\x8b\b\0\0\0\0\0\0\003\xec\xbdk{\xe3Æ(\x9c\xaf\xc2\xf3\xf0?\xb4\xa9I\x86\x90
\x8a
Dear Wagner,
I added the equamax rotation option to the psych package in version 1.4.6.
This was requested by Sagnik Chakravarty, with a solution by Gunter Nickel.
Unfortunately, the version on CRAN is 1.4.5, but you can get the working
prerelease of 1.4.6 (1.4.6.20) from the alternate repo
Dear Wagner,
I added the equamax rotation option to the psych package in version 1.4.6.
This was requested by Sagnik Chakravarty, with a solution by Gunter Nickel.
Unfortunately, the version on CRAN is 1.4.5, but you can get 1.4.6 from the
alternate repository http://personality-project.org
Hello,
I need to do a principal component analysis with EQUAMAX-rotation.
Unfortunately the function principal() I use normally for PCA does not offer
this rotation specification. I could find out that this might be possible
somehow with the package GPArotation but until now I could not figure out
x <- data.frame(a=rnorm(100), b=rnorm(100), d=rnorm(100))
prcomp(x, scale=T)
prcomp(scale(x), scale=F)
The above will give you the same thing. This should be the case because
the correlation matrix is the same as the covariance of the scaled and
centered original data.
FWIW
Stephen
On 02/2
Hi,
On Wed, Feb 29, 2012 at 9:52 AM, Blaz Simcic wrote:
> Dear R buddies,
> I’m trying to run Principal Component Analysis, package
> princomp:
> http://stat.ethz.ch/R-manual/R-patched/library/stats/html/princomp.html.
I'm going to assume you actually mean the princomp() function.
> My questio
Dear R buddies,
Iâm trying to run Principal Component Analysis, package
princomp:
http://stat.ethz.ch/R-manual/R-patched/library/stats/html/princomp.html.
My question is: why do I get different results with pca =
princomp (x, cor = TRUE) and pca = princomp (x, cor = FALSE) even when I
standardi
Alain Guillet-2 wrote:
>
> The PCA doesn't work with class variables so the error is normal. You
> should try to work with a discriminant factorial analysis (see
> discrimin.coa in ade4).
>
> Alain thanks for the tip but it did not work, well then I manaege to put
> it to work but the res
Hi, R users,
I'm using the "lme" function in R to estimate a 2 level mixed effects
model, in which the size of the groups are different. It turned out that It
takes forever for R to converge. I also tried the same thing in SPSS and
SPSS can give the results out within 20 minutes. Anyone can give
The PCA doesn't work with class variables so the error is normal. You
should try to work with a discriminant factorial analysis (see
discrimin.coa in ade4).
Alain
andreiabb wrote:
Dear Forum,
I have a class variable 1 (populations A-E), and two other class variables,
variable 2 and variabl
Dear Forum,
I have a class variable 1 (populations A-E), and two other class variables,
variable 2 and variable 3. What I want is to see if the combination of var 2
and var 3, will give me a pattern that allows to distinguish populations.
I found several packages like ade4, with pcaiv function a
; Email: rvarad...@jhmi.edu
>
> Webpage: http://www.jhsph.edu/agingandhealth/People/Faculty/Varadhan.html
>
>
>
> ---
>-
>
>
> -Original Message-
> From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On
> Behalf Of S Ellison
> Sent: Thursday, December 11,
Varadhan.html
-Original Message-
From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On
Behalf Of S Ellison
Sent: Thursday, December 11, 2008 9:37 AM
To: r-help@r-project.org; Corrado
Subject: Re: [R] Principal Component Analysi
If you're intending to create a model using PCs as predictors, select
the PCs based on whether they contribute significanctly to the model
fit.
In chemometrics (multivariate stats in chemistry, among other things),
if we're expecting 3 or 4 PC's to be useful in a principal component
regression, we
You can have look to
*S. Dray*. On the number of principal components: A test of
dimensionality based on measurements of similarity between matrices.
/Computational Statistics and Data Analysis/, 52:2228-2237, 2008.
which is implemented in the testdim function of the ade4 package.
Cheers.
Dear R gurus,
I have some climatic data for a region of the world. They are monthly averages
1950 -2000 of precipitation (12 months), minimum temperature (12 months),
maximum temperature (12 months). I have scaled them to 2 km x 2km cells, and
I have around 75,000 cells.
I need to feed them in
On Thu, 14 Feb 2008, SNN wrote:
>
> Thanks for the advice.
>
> I tried to find the cov of my matrix using R and it ran out of memory.
How did you do this? The covariance matrix is only 115x115, so it
shouldn't run out of memory
cov(t(code))
should work
If that doesn't work then
tcrossprod
> larger data sets where the entire data matrix doesn't fit in memory, you
> need some sort of double loop.
>
> -thomas
>
>
>> Zhaoming
>> -Original Message-
>> From: SNN [mailto:[EMAIL PROTECTED]
>> Sent: Wednesday, February 13, 2008 9
d some sort of double loop.
-thomas
> Zhaoming
> -Original Message-
> From: SNN [mailto:[EMAIL PROTECTED]
> Sent: Wednesday, February 13, 2008 9:14 PM
> To: r-help@r-project.org
> Subject: [R] Principal component analysis PCA
>
>
> Hi,
>
> I am tryin
Try EIGENSTRAT http://www.nature.com/ng/journal/v38/n8/abs/ng1847.html
or use a subset of SNPs.
Zhaoming
-Original Message-
From: SNN [mailto:[EMAIL PROTECTED]
Sent: Wednesday, February 13, 2008 9:14 PM
To: r-help@r-project.org
Subject: [R] Principal component analysis PCA
Hi,
I
Hi,
I am trying to run PCA on a set of data with dimension 115*300,000. The
columns represnt the snps and the row represent the individuals. so this is
what i did.
#load the data
code<-read.table("code.txt", sep='\t', header=F, nrows=30)
# do PCA #
pr<-prcomp(code, retx=T, center=T)
I a
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