Dear all
I am struggling to customise biplot from FactoMineR pacakge
My code is almost correct but piece of information is missing
Here is data
temp <- structure(list(leukocyte28 = c(96875L, 73438L, 68229L, 94479L,
76563L, 141667L, 111042L, 9L, 132083L, 103542L, 61667L, 77708
This is a statistical question, which is typically off topic here.
This list is primarily concerned with R programming questions,
although the two areas sometimes do intersect. I suggest you post on a
statistical list such as stats.stackexchange.com instead, especially
if you do not get a useful re
I have the results of a rating study in which ~30 participants rated a subset
of 20 items on 25 different dimensions.
I would like to perform PCA on these ratings to reduce the 25 different
dimensions. However, instead of doing this on the mean ratings for each
item, I would like to perform the PC
Off topic for this list.
Post on stats.stackexchange.com or similar for statistics questions.
Post on Bioconductor list for biology-related (e.g. proteomics) data
anaysis questions.
Cheers,
Bert
Bert Gunter
"The trouble with having an open mind is that people keep coming along
and sticking th
I'm working with proteomic data, helping a student who knows biology and
has done analysis in R without understanding it in depth.
We have 3000 protein levels for 6 ages. I can treat this as 6 vectors in
3000-dimensional space, diagonalize a 6x6 covariance matrix and find 5
principal components,
Mohsen,
Check at Bioconductor.
Andrés
> El 03/03/2016, a las 9:43, Mohsen Jafarikia escribió:
>
> Hello everyone:
>
> I have about a couple of thousands of samples each with about 100 SNP
> genotypes and I would like to do PCA using genotypes. I looked on the
> web and found different option
Hello everyone:
I have about a couple of thousands of samples each with about 100 SNP
genotypes and I would like to do PCA using genotypes. I looked on the
web and found different options available on R for PCA. I was
wondering if I could have advice about the program fits better what I
am trying
Of Boris Steipe
Sent: Monday, November 30, 2015 9:01 AM
To: debra ragland
Cc: r-help
Subject: Re: [R] PCA plot of variable names only
Please keep communications on list.
This is too confused to continue productively.
See here: http://adv-r.had.co.nz/Reproducibility.html
http://stackover
77840-4352
-Original Message-
From: R-help [mailto:r-help-boun...@r-project.org] On Behalf Of Boris Steipe
Sent: Monday, November 30, 2015 9:01 AM
To: debra ragland
Cc: r-help
Subject: Re: [R] PCA plot of variable names only
Please keep communications on list.
This is too confused to c
Please keep communications on list.
This is too confused to continue productively.
See here: http://adv-r.had.co.nz/Reproducibility.html
http://stackoverflow.com/questions/5963269/how-to-make-a-great-r-reproducible-example
... and please read the posting guide and don't post in HTML.
On Nov 3
> Any idea on how to generate such a plot based on this description?
One simple way of suppressing the individual points in biplot() is to give the
labels a colour of 0.
Adapting the biplot.princomp example:
biplot(princomp(USArrests), col=c(0,1))
But that retains the point plot axes. If
Your description is obscure but the following may get you started. The function
prcomp() returns a list in which the matrix x contains the rotated values of
your input. Assuming that your "variable names" are the rownames of your input,
you can plot them with text().
Something like (untested)
Hello,
A colleague of mine prepared a PCA plot of my data and I have no clue how he
did it. My original data set contains 15 variables and 64 observations. I have
been trying to figure out how he did it on my own, and I have asked but he's
swamped so his response is taking longer than usual. A
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
On Sun, 18 Jan 2015, Jackson Rodrigues wrote:
I have a matrix with 72 plant species, however most of them are irrelevant
or not very important. When they are displayed together my plot is
cluttered.So I want to constrain my biplot showing only species A B C D.
How can I write the right code?
J
Hi everybody,
I am using vegan package.
I have a matrix with 72 plant species, however most of them are irrelevant
or not very important. When they are displayed together my plot is
cluttered.So I want to constrain my biplot showing only species A B C D.
How can I write the right code?
I am tryin
Hi,
You have to transform it to a Data Frame.
Try:
files <- stack(rasterlist)
filesdf<-as.data.frame(files)
pca <- princomp(formula = ~., data = filesdf, cor = TRUE,
na.action=na.exclude)
hope it helps
Gustavo
Em quinta-feira, 30 de outubro de 2014 14h38min56s UTC-2, John Wasige
escreveu
Hello community, I need help on how I can perform PCA on stacked raster
(multiple bands/ layers) in R. Does any body have an idea or script? Thanks
John
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riginal Message-
From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On
Behalf Of Jim Silverton
Sent: 08 July 2014 17:30
To: r-help@r-project.org
Subject: Re: [R] PCA with a lot of zeros
Hello all,
I was wondering if R has some routine that can handle PCA with a lot of
Hello all,
I was wondering if R has some routine that can handle PCA with a lot of
zeros. I have fourteen variables - these variables represent angles...so
there are some negative and some positive angles. Histograms appear sparse
- in the sense that there are gaps. Any ideas or papers would be gre
That worked! Wow easy fix, i feel dumb! Thanks!
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___
half Of Andy.P
Sent: Tuesday, January 21, 2014 12:18 PM
To: r-help@r-project.org
Subject: [R] PCA factominer package, question about changing
labels in individuals factor map
I am looking to do a PCA with the factominer package on a
dataset of mine,
named dat. The individuals in my dataset have ch
I am looking to do a PCA with the factominer package on a dataset of mine,
named dat. The individuals in my dataset have character names, represented
in the first column of my dataset, but since they aren't quantitative I
can't include that column in my PCA analysis. Leading to the command >
res.pc
Hi,
see tutorial for Adegenet, http://adegenet.r-forge.r-project.org/ |
Documents | adegenet-basics.pdf | section 6. It should help You.
Vojtěch
-
Vojtěch Zeisek
Department of Botany, Faculty of Science, Charles Uni., Prague, CZ
Institute of Botany, Academy of Science, Czech Republic
Commun
ingston ON Canada
> -Original Message-
> From: a...@walla.co.il
> Sent: Wed, 10 Jul 2013 12:49:55 -0700 (PDT)
> To: r-help@r-project.org
> Subject: Re: [R] PCA and gglot2
>
> Dear John,
>
> Thanks for the help.
>
> I did some minor modifications to your
Hi,
Thanks to ssefick for the ggbiplot tip.
It works fine so I submit a general script thats works for future users.
library(ggbiplot)
data<-read.csv("C:/…/MyPCA.csv")
data1<-data[,1:4]
my.pca <- prcomp(data1, scale. = TRUE)
my.class<- data$Group
g <- ggbiplot(my.pca, obs.scale = 1, var.scal
Dear John,
Thanks for the help.
I did some minor modifications to your script as I had some problems:
...
pca = PCA(data[,1:4], scale.unit=T, graph=F)
dat1 <- data.frame(pca$scores) # creates the data.frame
dat1$items <- rownames(data$group) # adds item names
ggplot(dat1, aes(pca$ind$coord[
Fig 4 but there the author is using geom_segment to add the lines but I
> have not looked at it all that carefully.
>
>
>
>
>
> John Kane
> Kingston ON Canada
>
>
> > -Original Message-
> > From: a...@walla.co.il
> > Sent: Wed, 10 Jul 2013 11:02
Fig 4 but there the author is using geom_segment to add the lines but I
> have not looked at it all that carefully.
>
>
>
>
>
> John Kane
> Kingston ON Canada
>
>
> > -Original Message-
> > From: a...@walla.co.il
> > Sent: Wed, 10 Jul 2013 11:02
N Canada
> -Original Message-
> From: a...@walla.co.il
> Sent: Wed, 10 Jul 2013 11:02:11 -0700 (PDT)
> To: r-help@r-project.org
> Subject: Re: [R] PCA and gglot2
>
> Hi,
>
> Thanks. Fig 4 in the link you provided is what I am looking for.
>
> I still do no
Hi,
Thanks. Fig 4 in the link you provided is what I am looking for.
I still do not know how to implement my data1 and pca1 in the script you
provided as I think it is only a part of a full script.
"
data1<-read.csv("C:/…/MyPCA.csv")
pca1 <- princomp(data1[,1:4], score=TRUE, cor=TRUE)
"
Am I ri
> > The biplot present the data points as numbers. How can I
> present the
> > data point in color (depends on their group-column 5). I
> was thinking
> > about doing it using ggplot2 but I can not succeed. Any
> idea how to do
> > it?
Perhaps the post at
http://www.codesofmylife.com/2012/06
sage-
> From: a...@walla.co.il
> Sent: Wed, 10 Jul 2013 06:09:00 -0700 (PDT)
> To: r-help@r-project.org
> Subject: [R] PCA and gglot2
>
> Hi,
>
> I was trying as well as looking for an answer without success (a bit
> strange
> since it should be an easy problem) an
Hi,
I was trying as well as looking for an answer without success (a bit strange
since it should be an easy problem) and therefore I will appreciate you
help:
My simple script is:
# Loadings data of 5 columns and 100 rows of data
data1<-read.csv("C:/…/MyPCA.csv")
pairs(data1[,1:4])
pca1 <- princo
BOURGADE Eric
> Sent: Thursday, February 28, 2013 3:50 AM
> To: r-help@r-project.org
> Subject: [R] PCA with spearman and kendall correlations
>
> Hello,
>
> I would like to do a PCA with dudi.pca or PCA, but also with the use of
> Spearman or Kendall correlations
> Is it
Hello,
I would like to do a PCA with dudi.pca or PCA, but also with the use of
Spearman or Kendall correlations
Is it possible ?
Otherwise, how can I do, according to you ?
Thanking you in advance
Eric Bourgade
RTE
France
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4352
> -Original Message-
> From: r-help-boun...@r-project.org [mailto:r-help-bounces@r-
> project.org] On Behalf Of Tinus Sonnekus
> Sent: Sunday, September 09, 2012 1:37 PM
> To: r-help@r-project.org
> Subject: [R] PCA legend outside of PCA plot
>
> Hi All,
>
> I
Hi All,
I have been trying to get to plot my PCA legend outside of the PCA plot,
but success still alludes me.
Can you guys please advise how I can achieve this. I used locater() to
obtain coordinates for below the Comp.1 axis. Using these coordinates the
legend disappears.
Below is the code for
in biplot you could set the limits xlim, ylim of the axes to zoom in on the
plot.
-
Yasir Kaheil
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Hi everybody!
I do at this time PCA. Everything was going good, I have got good results.
Now I want make a plot. You can see it on the next image:
http://r.789695.n4.nabble.com/file/n4636840/Forum.jpg
For this plot I use the simple biplot function.
Now, it is not exactly what I want. I prefer t
The arrows are not pointing in the most-varying direction of the data. The
principal components are pointing in the most-varying direction of the
data. But you are not plotting the data on the original scale, you are
plotting the data on the rotated scale, and thus the horizontal axis is the
most
x=rmvnorm(2000, rep(0, 6), diag(c(5, rep(1,5
x=scale(x, center=T, scale=F)
pc <- princomp(x)
biplot(pc)
There are a bunch of red arrows plotted, what do they mean? I knew that the
first arrow labelled with "Var1" should be pointing the most varying
direction of the data-set (if
I could not reply directly to the initial thread with the same title.
There are two sorts of Robust PCA, those that were devised before the
recent string of Low Rank approaches and then the new set of algorithms
that provide robust PCA in light of sparse but potentially large
errors/outliers (typi
> "SL" == Steve Lianoglou
> on Mon, 23 Apr 2012 01:10:31 -0400 writes:
SL> On Mon, Apr 23, 2012 at 12:01 AM, Michael
SL> wrote:
>> yes, but that is not a good Review or Survey... thx
SL> But the packages listed there do have their own
SL> documentation and vignet
On Mon, Apr 23, 2012 at 12:01 AM, Michael wrote:
> yes, but that is not a good Review or Survey... thx
But the packages listed there do have their own documentation and
vignettes. For instance the rrcov package seems to have a nice
vignette about its design as well as methods it implements, and
r
yes, but that is not a good Review or Survey... thx
On Sun, Apr 22, 2012 at 9:47 PM, Bert Gunter wrote:
> As I believe I already told you, look at the CRAN Robust task view.
>
> -- Bert
>
> On Sun, Apr 22, 2012 at 6:29 PM, Michael wrote:
> > Even in R, there are so many of "robust PCA"... any s
As I believe I already told you, look at the CRAN Robust task view.
-- Bert
On Sun, Apr 22, 2012 at 6:29 PM, Michael wrote:
> Even in R, there are so many of "robust PCA"... any survey or review of all
> these different methods?
>
> On Sun, Apr 22, 2012 at 6:58 PM, Joshua Wiley wrote:
>
>> On Su
Even in R, there are so many of "robust PCA"... any survey or review of all
these different methods?
On Sun, Apr 22, 2012 at 6:58 PM, Joshua Wiley wrote:
> On Sun, Apr 22, 2012 at 4:43 PM, Michael wrote:
> > I actually tried "robustPca" in "pcaMethods" on bioconductor.
> >
> > It keeps giving me
On Sun, Apr 22, 2012 at 4:43 PM, Michael wrote:
> I actually tried "robustPca" in "pcaMethods" on bioconductor.
>
> It keeps giving me the warning "Input data is not complete"...
>
> Reading into the function:
>
> When there is no "NA"s, it will give this warning...
>
> It seems that there is a bu
Any thoughts on this error in robustSVD?
Thanks a lot!
Error in if (!all(tmp)) { : missing value where TRUE/FALSE needed
Enter a frame number, or 0 to exit
1: #73: pca(dTmp, method = "robustPca", nPcs = nNumFactors, center = FALSE)
2: robustPca(prepres$data, nPcs = nPcs, ...)
3: robustSvd(Mat
I actually tried "robustPca" in "pcaMethods" on bioconductor.
It keeps giving me the warning "Input data is not complete"...
Reading into the function:
When there is no "NA"s, it will give this warning...
It seems that there is a bug in this code...
Is it reliable at all?
You can also have a look at the pcaMethods package on Bioconductor.
Kevin
On Thu, Apr 19, 2012 at 11:20 PM, Michael wrote:
> Hi all,
>
> I found that the PCA gave chaotic results when there are big changes in a
> few data points.
>
> Are there "improved" versions of PCA in R that can help with
Michael:
On Thu, Apr 19, 2012 at 9:20 PM, Michael wrote:
> Hi all,
>
> I found that the PCA gave chaotic results when there are big changes in a
> few data points.
Yup.
>
> Are there "improved" versions of PCA in R that can help with this problem?
Yup.
Consult the "Robust" task view on CRAN. Yo
Hi all,
I found that the PCA gave chaotic results when there are big changes in a
few data points.
Are there "improved" versions of PCA in R that can help with this problem?
Please give me some pointers...
Thank you!
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___
Hello can anyone help,
I have been running the following script to obtain a PCA plot but the end
result is rather disappointing as the points are very very small and there are
no titles etc
geochemdata<-read.csv(file.choose(),header=TRUE)
names(geochemdata)
library(vegan)
bstick<-function(n,
Hi,
I have 6 variables and I want to do a PCA Kernel on the 6 variables. But I
want the scores from the from the PCA kernel method. for each subject. Does
anyone know how to do this?
--
Thanks,
Jim.
[[alternative HTML version deleted]]
__
R-h
Without taking away all the fun of trial and error, and exploration in R... I
will direct you to this website which I found invaluable when I first began
to use R.
one way would be to use:
plot(Yourdata, type="n")
and then 3 text() or points() statements to plot the groups represented by
differen
Hi
This has a simple answer but it has been eluding me nonetheless.
I have been trying to build a PCA plot from scratch with the ability to plot
predefined groups in different colors. I can plot PCA but I want it to plot
with predefined groups(samples) with top 100 expressed genes. I have three
I am doing Principal Component Analysis (PCA) on assets data for household
income prediction. The problem is that the assets data are rank ordered
(usually binary ... possess car/don't possess car), so the normal correlation
is inappropriate for the calculation of the PCA. Instead one has to use
You'll get more useful answers if you tell us what you did, and provide a
reproducible example.
For instance, a bit of your data using dput(), your sessionInfo(), str() for
your data, and the actual commands you're using to run PCA, as well
as the error messages you're getting.
The clearer you ar
Hello
Would anyone be able to direct me to information on how to perform a
straightforward Principal Components Analysis in r? Including the data file
set-up?
I'm rather new to r and not having much luck.
I'm pretty certain I have the data entered into the txt file properly but when
I try to
... and adding to what has already been said, PCA can be distorted by
non-ellipsoidal distributions or small numbers of unusual values.
Careful (chiefly graphical) examination of results is therefore
essential, and usually fairly easy to do. There are robust/resistant
versions of PCA in R, but they
On Dec 10, 2011 at 5:56pm deb wrote:
> My question is, is there any way I can map the PC1, PC2, PC3 to the
> original conditions,
> so that i can still have a reference to original condition labels after
> PCA?
deb,
To add to what Stephen has said. Best to do read up on principal component
anal
By doing PCA you are trying to find a lower dimensional representation
of the major variation structure in your data. You get PC* to represent
the "new" data. If you want to know what loads on the axes then you
need to look at the loadings. These are the link between the original
data and th
Hi:
I have a large dataset mydata, of 1000 rows and 1000 columns. The rows
have gene names and columns have condition names (cond1, cond2, cond3,
etc).
mydata<- read.table(file="c:/file1.mtx", header=TRUE, sep="")
I applied PCA as follows:
data_after_pca<- prcomp(mydata, retx=TRUE, center=TRUE,
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Did you perhaps send an HTML message? As detailed in the Posting
Guide, those get scrubbed by the mail-server.
On Oct 21, 2011, at 10:48 AM, seanstcl...@verizon.net wrote:
--
David Winsemius, MD
West Hartford, CT
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
Hi all,
I think I may be confused by different people/programs using the word
rotation differently.
Does prcomp not perform rotations by default?
If I understand it correctly retx=TRUE returns ordinated data, that I can
plot for individual samples (prcomp()$x: which is the scaled and centered
(rot
> -Original Message-
> christopher stratton
> Sent: 14 August 2011 22:22
> Subject: [R] PCA Using prcomp()
>
> From the results
> generated by prcomp(), is there a way to print a matrix
> showing the contributions of the original variables to each
> PC?
Sou
Hey guys,
I am new to R and apologize for the basic question - I do not mean to
offend.
I have been using R to perform PCA on a set several hundred objects using a
set of 30 descriptors. From the results generated by prcomp(), is there a
way to print a matrix showing the contributions of the orig
Hi Armin,
Please copy the list on your emails. Providing your matrix A (or some
other reproducible example) would be useful to anyone who wanted to
help you. It is easy to do by copying the output from your console
from running:
dput(A)
This would at least let us try out your code on your data
At 03:56 20/07/2011, Joshua Wiley wrote:
On Mon, Jul 18, 2011 at 10:48 AM, a.me...@yahoo.co.uk
wrote:
> Ok thank you Josh.
>
> Basically I have a matrix A with 7 rows and 18 columns.
If i < j (where i is the number of rows in your matrix and j is the
number of columns), then the determinant of
On Mon, Jul 18, 2011 at 10:48 AM, a.me...@yahoo.co.uk
wrote:
> Ok thank you Josh.
>
> Basically I have a matrix A with 7 rows and 18 columns.
If i < j (where i is the number of rows in your matrix and j is the
number of columns), then the determinant of the covariance (or
correlation) matrix |Sig
Hi,
You need to explain what you want to do. This is not a software
issue, you simply cannot create more uncorrelated variables than you
have observations.
Josh
On Mon, Jul 18, 2011 at 8:53 AM, a.me...@yahoo.co.uk
wrote:
> Hi,
>
> May I ask a question about a thread
> https://stat.ethz.ch/pipe
Hi,
May I ask a question about a thread
https://stat.ethz.ch/pipermail/r-help/2005-March/068365.html?
I understand I need to use prcomp instead of princomp when i have less
units than variables.
However, when I use prcomp the scores is NULL. How can I overcome this?
Regards,
Armin
--
Kind
On 04.03.2011 17:52, Shari Clare wrote:
Hi Bill and Josh:
When I run any "principal" code with scores=TRUE, I get the following
Error:
Error in principal (my.data,3,scores=TRUE) : unused argument
(scores=TRUE)
Thoughts?
Your psych version (and probably also your R version) is outdated?
Ple
Hi Bill and Josh:
When I run any "principal" code with scores=TRUE, I get the following
Error:
Error in principal (my.data,3,scores=TRUE) : unused argument
(scores=TRUE)
Thoughts?
Thanks,
Shari
On 3-Mar-11, at 9:42 PM, William Revelle wrote:
> Shari,
> Josh partly answered your ques
At 9:52 AM -0700 3/4/11, Shari Clare wrote:
Hi Bill and Josh:
When I run any "principal" code with scores=TRUE, I get the following Error:
Error in principal (my.data,3,scores=TRUE) : unused argument (scores=TRUE)
Thoughts?
What version of psych are you using?
Does it work on the example I
Shari,
Josh partly answered your question, but his example did not include
rotation because he took out just one factor.
Try:
require(psych)
mt.pc <- principal(mtcars,3,scores=TRUE) #this gives you the
varimax rotated first 3 principal components
#pc.scores <- mt.pc$scores #here are
Hi Shari,
Yes, please look at the documentation for principal. You can access
this (assuming you have loaded psych) by typing at the console:
?principal
note the logical argument "scores".
Here is a small example:
##
require(psych)
require(GPArotation)
dat <- prin
I am running a PCA, but would like to rotate my data and limit the
number of factors that are analyzed. I can do this using the
"principal" command from the psych package [principal(my.data,
nfactors=3,rotate="varimax")], but the issue is that this does not
report scores for the Principal
Hi He Zhang,
>> Is the following right for extracting the scores?
>> ...
>> pca$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
PCA components are orthogonal by definition so no, that doesn't make
sense at all. Do yourself a favor and get a book on multivariate data
analysis. Two books come to mind: Obviously the one of Hair and
colleagues, called "multivariate data analysis" and easily found in a
university library (or on
Hello,
I am currently analyzing responses to questionnaires about general attitudes. I
have performed a PCA on my data, and have retained two Principal Components.
Now I would like to use the scores of both the principal comonents in a
multiple regression. I would like to know if it makes sense
On Fri, 2010-04-16 at 10:23 -0700, phoebe kong wrote:
> Hi all,
>
> I have a difficulty to calculate the PCA scores. The PCA scores I calculated
> doesn't match with the scores generated by R,
>
> mypca<-princomp(mymatrix, cor=T)
>
> myscore<-as.matrix(mymatrix)%*%as.matrix(mypca$loadings)
>
>
Hi all,
I have a difficulty to calculate the PCA scores. The PCA scores I calculated
doesn't match with the scores generated by R,
mypca<-princomp(mymatrix, cor=T)
myscore<-as.matrix(mymatrix)%*%as.matrix(mypca$loadings)
Does anybody know how the mypca$scores were calculated? Is my formula not
Hi All,
I have been running pca smoothly for some time now. However, the error
message below makes progress difficult for me.
It will be much appreciated if anybody can hint me on the possible source of
this error.
Thanks
Ogbos
The error:
Error in pca$rotation %*% sqrt(data.cor.eigen.matrix) :
no
On 17.03.2010 00:16, Xanthe Walker wrote:
Hi,
I have successfully completed a PCA and printed the loadings, however,
numerous values are blank. I know that this means the values are just very
small but not equal to zero.
Is there a way to print out the loadings, including the very small value
Which principal component function are you using?
Check the documentation for that and look for the part of the object
that provides the PC's. Those are your loadings.
>>> Xanthe Walker 16/03/2010 23:16:47 >>>
Hi,
I have successfully completed a PCA and printed the loadings, however,
numerous v
Hi,
I have successfully completed a PCA and printed the loadings, however,
numerous values are blank. I know that this means the values are just very
small but not equal to zero.
Is there a way to print out the loadings, including the very small values, I
need them for graphing purposes.
Thanks,
On Wed, Mar 10, 2010 at 4:42 PM, Xanthe Walker wrote:
> Hello,
>
> I am trying to complete a PCA on a set of standardized ring widths from 8
> different sites (T10, T9, T8, T7, T6, T5, T3, and T2).
> The following is a small portion of my data:
>
> T10 T9 T8 T7 T6 T5 T3 T2 1.33738 0.92669 0.91146
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