en
> > x = -x
> > In this case the positive values will become negative and the negative
> > values
> > positive.
> > Add an if test to selectively rotate based on the value of a single test
> > element in x
> > (as in x[3,2]).
> >
> > In debugging or troub
se set.seed(NULL).
>
> Tim
>
>
>
> -Original Message-
> From: Ashim Kapoor
> Sent: Thursday, October 13, 2022 12:28 AM
> To: Ebert,Timothy Aaron
> Cc: R Help
> Subject: Re: [R] prcomp - arbitrary direction of the returned principal
> components
>
&g
2]).
In debugging or trouble shooting setting seed is useful. For actual data
analysis you should not set seed, or possibly better yet use set.seed(NULL).
Tim
-Original Message-
From: Ashim Kapoor
Sent: Thursday, October 13, 2022 12:28 AM
To: Ebert,Timothy Aaron
Cc: R Help
Subject:
В Wed, 12 Oct 2022 17:18:26 +0530
Ashim Kapoor пишет:
> My problem is that I am building an index based on Principal
> Components Analysis.
> When the index is high it should indicate stress in the market.
Have you considered using supervised methods, like PLS, to predict
stress in the market?
,1] is the solution.
> Yes it will make the results REPRODUCIBLE but that will be at the
> cost
> of losing information.
>
> Any other idea ?
>
> Many thanks,
> Ashim
>
> On Wed, Oct 12, 2022 at 5:23 PM Ebert,Timothy Aaron
> wrote:
> >
> > Use absol
> -Original Message-
> From: R-help On Behalf Of Ashim Kapoor
> Sent: Wednesday, October 12, 2022 7:48 AM
> To: R Help
> Subject: [R] prcomp - arbitrary direction of the returned principal components
>
> [External Email]
>
> Dear R experts,
>
> From ?prcomp,
>
Use absolute value
Tim
-Original Message-
From: R-help On Behalf Of Ashim Kapoor
Sent: Wednesday, October 12, 2022 7:48 AM
To: R Help
Subject: [R] prcomp - arbitrary direction of the returned principal components
[External Email]
Dear R experts,
>From ?prcomp,
snip -
N
Dear R experts,
>From ?prcomp,
snip -
Note:
The signs of the columns of the rotation matrix are arbitrary, and
so may differ between different programs for PCA, and even between
different builds of R.
snip --
My problem is that I am building an index based on Pr
Dear R community,
I have a data matrix (531X314), and would like to apply the prcomp. However, I
got this error Lapack message. I am using R3.2.2.
I googled a bit and found that it might be related to converge issue. Just
wonder if there is a way to get around it?
Thank you very much!
Ace
thropology
Texas A&M University
College Station, TX 77840-4352
-Original Message-
From: R-help [mailto:r-help-boun...@r-project.org] On Behalf Of Bert Gunter
Sent: Wednesday, November 9, 2016 10:58 AM
To: T.Riedle
Cc: R-help@r-project.org
Subject: Re: [R] prcomp() on correlation matrix
We
help [mailto:r-help-boun...@r-project.org] On Behalf Of T.Riedle
Sent: Wednesday, November 9, 2016 6:46 AM
To: R-help@r-project.org
Subject: [R] prcomp() on correlation matrix
Dear R users,
I am trying to do a Principal Components Analysis using the prcomp() function
based on the correlation ma
Well, it seems you can't -- prcomp() seems to want the data matrix.
But it would be trivial using svd() -- or possibly even eigen() -- if
you understand the underlying linear algebra.
Cheers,
Bert
Bert Gunter
"The trouble with having an open mind is that people keep coming along
and sticking th
Dear R users,
I am trying to do a Principal Components Analysis using the prcomp() function
based on the correlation matrix. How can I determine to calculate PCA on a
correlation or covariance matrix using prcomp()?
Thanks in advance.
[[alternative HTML version deleted]]
You need to check your theory, and the dimensions of your data structures.
Typically, data is (n x p) and your rotation matrix is (p x p) so
pre-multiplying by coef1 fits like a round peg in a square hole.
Post-multiplying has a better chance, but I have long forgotten whether you
need to tran
Dear R-users,
I am trying to do a principal components analysis using the attached data. My
code looks as follows. I want to calculate the time series of the principal
components (PC) . To this end, I transform the coefficients and the data into
matrices and employ a matrix multiplication but i
On Oct 3, 2013, at 16:30 , Hermann Norpois wrote:
> Thanks for answering.
>
> I already started hunting. But my first doubt was if I used prcomp correctly
> (and this is in the moment my most important point). So far as I understood
> your answer is yes. Is that correct?
Yes. There are a cou
Thanks for answering.
I already started hunting. But my first doubt was if I used prcomp
correctly (and this is in the moment my most important point). So far as I
understood your answer is yes. Is that correct?
I am puzzled by the fact that these "columns" are more or less in the
middle of my sn
It's not so obvious to me that this is an artifact. What prcomp() says is that
some of the eigenvectors have a lot of "activity" in some relatively narrow
ranges of SNPs (on the same chromosome, perhaps?). If something artificial is
going on, I could imagine effects not so much of centering colu
Hello,
I did a pca with over 20 snps for 340 observations (ids). If I plot the
eigenvectors (called rotation in prcomp) 2,3 and 4 (e.g. plot
(rotation[,2]) I see a strange "column" in my data (see attachment). I
suggest it is an artefact (but of what?).
Suggestion:
I used prcomp this way: prc
ciate Professor of Anthropology
Texas A&M University
College Station, TX 77840-4352
-Original Message-
From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On
Behalf Of Bob Wiley
Sent: Friday, April 26, 2013 4:33 PM
To: r-help@r-project.org
Subject: [R] prcomp( and cmds
Hello,
I have a dilemma that I'm hoping the R gurus will be able to help resolve.
For background:
My data is in the form of a (dis)similarity matrix created from taking the
inverse of normalized reaction times. That is, each cell of the matrix
represents how long it took to distinguish two stimuli
On Apr 7, 2013, at 16:06 , Mike Amato wrote:
> Thanks for the reply. Maybe my problem is that prcomp() and varimax()
> are calculating "cumulative proportion of variance" differently?
> When I use the tol parameter with prcomp(), it restricts the number of
> components to 3 and reports that the
Thanks for the reply. Maybe my problem is that prcomp() and varimax()
are calculating "cumulative proportion of variance" differently?
When I use the tol parameter with prcomp(), it restricts the number of
components to 3 and reports that the cumulative variance explained by
the third component
>
> My concern is with the reported proportions of variance for the 3
> components after varimax rotation. It looks like each of my 3 components
> explains 1/15 of the total variance, summing to a cumulative proportion
> of 20% of variance explained. But those 3 components I retained should
>
Hello,
I am attempting to do a principal components analysis on 15 survey
items. I want to use a varimax rotation on the retained components, but
I am dubious of the output I am getting, and so I suspect I am doing
something wrong. I proceed in the following steps:
1) use prcomp() to inspect
roject.org [mailto:r-help-bounces@r-
> project.org] On Behalf Of Adams, Sky
> Sent: Wednesday, June 20, 2012 3:15 PM
> To: r-help@r-project.org
> Subject: [R] prcomp: where do sdev values come from?
>
> In the manual page for prcomp(), it says that sdev is "the standard
&g
Hi,
If center=T (by default) in invoking prcomp, that is, prcomp (x) where x is a
matrix with the observations are in rows and the variables are in column, is
this equivalent to scale(t(x),center=T,scale=F) where x is a matrix with the
observations are in rows and the variables are in columns?
In the manual page for prcomp(), it says that sdev is "the standard
deviations of the principal components (i.e., the square roots of the
eigenvalues of the covariance/correlation matrix, though the
calculation is actually done with the singular values of the data
matrix)." However, this is not wh
In addition, many PCA packages follow the convention that if the majority of
weights are negative for that component, reverse the sign.
On 2011-09-10, at 4:00 AM, r-help-requ...@r-project.org wrote:
> The point is that a principal component vector is a solution,
> say V, of a matrix equation A%*
thanks for explaining Duncan and Ted,
Indeed, I did compare my results from a textbook
and noticed that I consitenly get flipped signs and biplots.
regards
René
Zitat von ted.hard...@wlandres.net:
The point is that a principal component vector is a solution,
say V, of a matrix equation A%*%V
The point is that a principal component vector is a solution,
say V, of a matrix equation A%*%V = L*V where A is the matrix
and L is a scalar..
Since this equation can be written A%*%(-V) = L*(-V), the
result is indeterminate with respect to its sign. If V is a
solution, so is (-V), and vice versa
On 11-09-09 5:42 AM, René Mayer wrote:
thanks for pointing out Paul,
but the thing which is annoying me in the first place IS this
direction reversal.
this makes no sense for me
why could this be?
I think you need to read more about principal components. The signs
within a PC vector are meani
thanks for pointing out Paul,
but the thing which is annoying me in the first place IS this
direction reversal.
this makes no sense for me
why could this be?
Zitat von "Paul Hiemstra" :
Hi,
If all the signs are switched the PC's are still the same. The principal
vectors are along the same
Hi,
If all the signs are switched the PC's are still the same. The principal
vectors are along the same axis, only in a different direction. So there
is no problem :).
hope this helps,
Paul
On 09/09/2011 09:01 AM, René Mayer wrote:
> Dear All,
>
> when I'm running a PCA with
>
> prcomp(USArrest
Dear All,
when I'm running a PCA with
prcomp(USArrests, scale = TRUE)
I get the right principal components, but with the wrong sign infront
Rotation:
PC1 PC2 PC3 PC4
Murder 0.5358995 -0.4181809 0.3412327 0.64922780
Assault 0.5831836 -0.1879856 0.2681484 -0.74340748
UrbanPop 0.2781909 0.8728062
Texas A&M University
College Station, TX 77843-4352
-Original Message-
From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On
Behalf Of David Winsemius
Sent: Wednesday, August 17, 2011 5:03 PM
To: David Winsemius
Cc: r-help@r-project.org; Rosario Garcia Gil
Subj
On Aug 17, 2011, at 5:47 PM, David Winsemius wrote:
On Aug 17, 2011, at 5:19 PM, Rosario Garcia Gil wrote:
Hello
I am trying to run a PCA on the attached file, but I get this error
message:
pc<-prcomp(data[,-(1:2)],scale=T)$x
Error in svd(x, nu = 0) : infinite or missing values in 'x'
On Aug 17, 2011, at 5:19 PM, Rosario Garcia Gil wrote:
Hello
I am trying to run a PCA on the attached file, but I get this error
message:
pc<-prcomp(data[,-(1:2)],scale=T)$x
Error in svd(x, nu = 0) : infinite or missing values in 'x'
What part of "missing values in 'x'" is unclear in tha
Hello
I am trying to run a PCA on the attached file, but I get this error message:
pc<-prcomp(data[,-(1:2)],scale=T)$x
Error in svd(x, nu = 0) : infinite or missing values in 'x'
Thanks in advance
/R x y x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11 x12 x13 x14
1 25.49 45.62 125 156 165 130 179 152 82 165
On May 28, 2011, at 20:08 , Natalie Stephenson wrote:
> Hi ...
>
> Please could you help with probably a very simple problem I have. I'm
> completely new to R and am trying to follow a tutorial using R for Force
> Distribution Analysis that I got from ...
> http://projects.eml.org/mbm/websit
Hi ...
Please could you help with probably a very simple problem I have. I'm
completely new to R and am trying to follow a tutorial using R for Force
Distribution Analysis that I got from ...
http://projects.eml.org/mbm/website/fda_gromacs.htm. Basically, the MDS I
preform outputs a force m
Dear Claudia,
you are right. Thank you very much for your explanations. So in the
non-centered case SDEV does not contain the "square roots of the eigenvalues
of the covariance/correlation matrix". In in the centered case it holds
A´A=(n-1)*cov(A) (not n+1).
Have a nice day.
--
View this messa
I think PCA decomposes matrix A according to A'A, not to COV (A).
But if A is centered then A'A = (n + 1) COV (A).
So for non-centered A, you want to look at A'A instead:
> crossprod(A) %*% evec[,1] / (nrow (A) - 1) - eval [1] * evec [,1]
[,1]
[1,] 0.000e+00
[2,] 0.000e+00
[3,] 1.066e
Hello,
I have a short question about the prcomp function. First I cite the
associated help page (help(prcomp)):
"Value:
...
SDEV the standard deviations of the principal components (i.e., the square
roots of the eigenvalues of the covariance/correlation matrix, though the
calculation is actually
What do you mean by 'lenght'? It is part of the definition that the
coefficient vector has Euclidean length one: a principal component is
a projection. See for example MASS p.302.
I don't see anything that has length 1 in the R sense.
On Wed, 16 Jun 2010, Atte Tenkanen wrote:
Hi,
I would
Hi,
I would like to know whether there is some deeper rationale behind or is it
just an established practice that the lenghts of principal components, giving
for example by prcomp-function, are normalised to 1?
Best regards,
Atte Tenkanen
University of Turku, Finland
Department of Musicology
+
First, this is about biplot, not prcomp.
Second, you seem to want to get a single-variable plot out of a
biplot, which contradicts the 'bi' and hence I would not expect there
to be a simple way to do this.
The simplest thing to do would be to edit biplot.default via
biplot.default <- stats::
Dear all,
I have a very large dataset (1712351 , 20) and would like
to plot only the arrows that represent the
contribution of each variables.
On the sample below I woild like to plot
only the explanatory variables (Murder, Assault..)
and not the sites.
prcomp(USArrests) # inappropriate
prcomp(U
Hi all,
I wonder what the difference is between the functions prcomp and the PCA
plotting method used in example 3 from the fastICA package. They give totally
different plots. The reason for asking is that I've earlier used prcomp, but
now I should do an ICA, and I guess I cannot compare th
The output of summary prcomp displays the cumulative amount of variance explained
relative to the total variance explained by the principal components PRESENT in the
object. So, it is always guaranteed to be at 100% for the last principal component
present. You can see this from the code in s
> cuncta stricte discussurus
> -
>
> -Ursprüngliche Nachricht-
> Von: [1]r-help-boun...@r-project.org
[[2]mailto:r-help-boun...@r-project.org] Im
> Auftrag von zubin
> Gesendet: Monday, November 09, 2009 12:37
---
cuncta stricte discussurus
-
-Ursprüngliche Nachricht-
Von: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] Im
Auftrag von zubin
Gesendet: Monday, November 09, 2009 12:37 PM
An: r-help@r-project.org
Betreff: [R] prcomp - principal compone
-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] Im
Auftrag von zubin
Gesendet: Monday, November 09, 2009 12:37 PM
An: r-help@r-project.org
Betreff: [R] prcomp - principal components in R
Hello, not understanding the output of prcomp, I reduce the number of
components and the output co
Look at it linearly?
On Mon, Nov 9, 2009 at 11:45 AM, zubin wrote:
> okay, an extreme case, only 1 component, explains 100%, something weird
> going on..
>
> > princ = prcomp(df[,-1],rotate="varimax",scale=TRUE,tol=.95)
> > summary(princ)
> Importance of components:
> PC1
okay, an extreme case, only 1 component, explains 100%, something weird
going on..
> princ = prcomp(df[,-1],rotate="varimax",scale=TRUE,tol=.95)
> summary(princ)
Importance of components:
PC1
Standard deviation 1.38
Proportion of Variance 1.00
Cumulative Proportion
principal components is a data reduction technique. It looks like
you have three axes that account for 100%. Make this reporducible.
On Mon, Nov 9, 2009 at 11:37 AM, zubin wrote:
> Hello, not understanding the output of prcomp, I reduce the number of
> components and the output continues to sh
Hello, not understanding the output of prcomp, I reduce the number of
components and the output continues to show cumulative 100% of the
variance explained, which can't be the case dropping from 8 components
to 3.
How do i get the output in terms of the cumulative % of the total
variance, so
Dear Agustin & the Listers,
Noncentred PCA is an old and establishes method. It is rarely used,
but still (methinks) it is used more often than it should be used.
There is nothing wrong in having noncentred PCA in R, and it is a real
PCA. Details will follow.
On 08/03/2009, at 11:07 AM, A
Hi Agus,
>> But the rotation made with the eigenvectors of prcomp(X,center=F) yields
>> axes that are correlated. Therefore, prcomp(X,center=F) is not really a
>> PCA.
cor() is not an appropriate test of whether two vectors are orthogonal. The
definition that two vectors (in an inner product sp
I do not understand, from a PCA point of view, the option center=F
of prcomp()
According to the help page, the calculation in prcomp() "is done by a
singular value decomposition of the (centered and possibly scaled) data
matrix, not by using eigen on the covariance matrix" (as it's done by
p
On 2/10/08, Erin Hodgess <[EMAIL PROTECTED]> wrote:
> When performing PCA, should I use prcomp, princomp or fast.prcomp, please?
You can take a look here [1] and here [2] for some short references.
>From the first page: "Principal Components Analysis (PCA) is available
in prcomp() (preferred) and
Hi R People:
When performing PCA, should I use prcomp, princomp or fast.prcomp, please?
thanks.
Erin
--
Erin Hodgess
Associate Professor
Department of Computer and Mathematical Sciences
University of Houston - Downtown
mailto: [EMAIL PROTECTED]
__
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