Displaying 20 results from an estimated 20000 matches similar to: "princomp output ??"
2012 Jan 24
0
PCA for assets based household income analysis (" hetcor" and "princomp")
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 the polychoric correlation coefficient. It uses the "random.polychor.pa" package.
2006 Jun 26
1
princomp and prcomp confusion
When I look through archives at
https://stat.ethz.ch/pipermail/r-help/2003-October/040525.html
I see this:
Liaw, Andy wrote:
>In the `Detail' section of ?princomp:
>
>princomp only handles so-called Q-mode PCA, that is feature extraction of
>variables. If a data matrix is supplied (possibly via a formula) it is
>required that there are at least as many units as variables. For
2006 Mar 25
1
Suggest patch for princomp.formula and prcomp.formula
Dear all,
perhaps I am using princomp.formula and prcomp.formula in a way that
is not documented to work, but then the documentation just says:
formula: a formula with no response variable.
Thus, to avoid a lot of typing, it would be nice if one could use '.'
and '-' in the formula, e.g.
> library(DAAG)
> res <- prcomp(~ . - case - site - Pop - sex, possum)
2004 Nov 03
2
Princomp(), prcomp() and loadings()
In comparing the results of princomp and prcomp I find:
1. The reported standard deviations are similar but about 1% from
each other, which seems well above round-off error.
2. princomp returns what I understand are variances and cumulative
variances accounted for by each principal component which are
all equal. "SS loadings" is always 1.
3. Same happens
2003 May 06
2
R vs SPSS output for princomp
Hi,
I am using R to do a principal components analysis for a class
which is generally using SPSS - so some of my question relates to
SPSS output (and this might not be the right place). I have
scoured the mailing list and the web but can't get a feel for this.
It is annoying because they will be marking to the SPSS output.
Basically I'm getting different values for the component
2009 Oct 19
2
What is the difference between prcomp and princomp?
Some webpage has described prcomp and princomp, but I am still not
quite sure what the major difference between them is. Can they be used
interchangeably?
In help, it says
'princomp' only handles so-called R-mode PCA, that is feature
extraction of variables. If a data matrix is supplied (possibly
via a formula) it is required that there are at least as many
units as
2005 Mar 24
0
Mapping actual to expected columns for princomp object
I am working with data sets in which the number and order of columns
may vary, but each column is uniquely identified by its name. E.g.,
one data set might have columns
MW logP Num_Rings Num_H_Donors
while another has columns
Num_Rings Num_Atoms Num_H_Donors logP MW
I would like to be able to perform a principal component analysis (PCA)
on one data set and save the PCA object to
2009 Nov 25
1
which to trust...princomp() or prcomp() or neither?
According to R help:
princomp() uses eigenvalues of covariance data.
prcomp() uses the SVD method.
yet when I run the (eg., USArrests) data example and compare with my own
"hand-written" versions of PCA I get what looks like the opposite.
Example:
comparing the variances I see:
Using prcomp(USArrests)
-------------------------------------
Standard deviations:
[1] 83.732400 14.212402
2005 May 23
1
Can't reproduce clusplot princomp results.
Dear R folk:
Perhaps I'm just dense today, but I am having trouble reproducing the
principal components plotted and summarized by clusplot. Here is a brief
example using the pluton dataset. clusplot reports that the first two
principal components explain 99.7% of the variability. But this is not what
princomp is reporting. I would greatly appreciate any advice.
With best regards,
-- Tom
2005 Mar 24
1
RE: [R] Mapping actual to expected columns for princomp object
[Re-directing to R-devel, as I think this needs changes to the code.]
Can I suggest a modification to stats:predict.princomp so that it will check
for column (variable) names?
In src/library/stats/R/princomp-add.R, insert the following after line 4:
if (!is.null(cn <- names(object$center))) newdata <- newdata[, cn]
Now Dana's example looks like:
> predict(pca1, frz)
Error in
2004 Sep 14
3
Signs of loadings from princomp on Windows
I start a clean session of R 1.9.1 on Windows and I run the following code:
> library(MASS)
> data(painters)
> pca.painters <- princomp(painters[ ,1:4])
> loadings(pca.painters)
Loadings:
Comp.1 Comp.2 Comp.3 Comp.4
Composition 0.484 -0.376 0.784 -0.101
Drawing 0.424 0.187 -0.280 -0.841
Colour -0.381 -0.845 -0.211 -0.310
Expression 0.664 -0.330 -0.513
2007 Apr 23
3
Help about princomp
Hello,
I have a problem with the princomp method, it seems stupid but I don't know
how to handle it.
I have a dataset with some regular data and some outliers. I want to
calculate a PCA on the regular data and get the scores for all data,
including the outliers. Is this possible on R?
Thank you for helping!!!
--
View this message in context:
2012 Apr 25
1
pca biplot.princomp has a bug?
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 we think them as 2000 data points, each being
a vector of size 6). I also read from
2008 May 14
1
PCA in Microarrays
Dear useRs:
I'm not sure if it's the correct place to ask but I'll try it out. I've been
reading about how to perform Principal Component Analysis (PCA) in
microarrays (see [1]) and there's something that I don't get it. Basically
it's related with performing PCA over data sets which number of variables is
greater than the number of samples. For example in the paper
2011 Jun 30
2
sdev value returned by princomp function (used for PCA)
Dear all,
I have a question about the 'sdev' value returned by the princomp function (which does principal components analysis).
On the help page for princomp it says 'sdev' is 'the standard deviations of the principal components'.
However, when I calculate the principal components for the USArrests data set, I don't find this to be the case:
Here is how I
2012 Apr 20
1
Quick question about princomp/biplot
Hi everyone.
I performing a simple PCA using the princomp function. Then, I use the
biplot function to show it. However, the function use line number to
represent samples. I would like to know if there's a way to use a dot
(point) instead of the line number when using the biplot function.
With regards,
Phil
--
View this message in context:
2005 Mar 26
5
PCA - princomp can only be used with more units than variables
Hi all:
I am trying to do PCA on the following matrix.
N1 N2 A1 A2 B1 B2
gene_a 90 110 190 210 290 310
gene_b 190 210 390 410 590 610
gene_c 90 110 110 90 120 80
gene_d 200 100 400 90 600 200
>dataf<-read.table("matrix")
>
2008 Feb 10
1
prcomp vs. princomp vs fast.prcomp
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: erinm.hodgess at gmail.com
2005 Jan 15
2
Newbie question regarding graphing of Princomp object
Greetings,
I am working on a stylometric analysis of some latin texts; one of the
latest stylometric techniques involves using principal components
analysis. Not being a statistician, I can't really fully rely on PCA
as my primary tool, since I don't really understand the statistics
behind the PCA technique. Nevertheless, the ability to use PCA and
graph the results has been
2007 May 10
1
A simple question about PRINCOMP
Hi,
I just wonder if this is a rounding error by the princomp command in R.
Although this does not make much sense, using a hypothetical dataset, a,
a<-matrix(runif(1000),100,10)
I did PCA with the princomp, and compared it with the results estimated
with the eigen and the prcomp commands. And I found some differences in
the results: opposite signs in the loadings; slight differences in