Re: [R] PCA IN R

[Bjørn-Helge Mevik]

prcomp() in stats handles matrices with n  p well, IMO.

-- 
Bjørn-Helge Mevik

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Re: [R] PCA in R

[Jonathan Baron]

On 07/14/04 16:05, Herman, David (NIH/NIMH) wrote:
Hello,
I'm attempting to run a PCA on an example data set. I ran it
just fine, but I don't know how to few the output?

Take a look at the help file for prcomp, especially the bottom of
it.  (This is completely general advice for any R function.)  You
will see under See also a number of things listed, and if you
look them up or try them, then you will see several different
ways of viewing the result.  Under that is Examples, which
provides more hints still.  And, if this isn't enough, type

example(prcomp)

on the command line.  The biplot function is especially nice.

Jon
-- 
Jonathan Baron, Professor of Psychology, University of Pennsylvania
Home page:http://www.sas.upenn.edu/~baron
R search page:http://finzi.psych.upenn.edu/

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RE: [R] PCA in R

[Liaw, Andy]

When all else fails, RTFM; e.g., see ?princomp and read it in its entirety.
Then maybe also try running example(princomp).

Andy

 From: Herman, David (NIH/NIMH)
 
 Hello,
 I'm attempting to run a PCA on an example data 
 set. I ran it
 just fine, but I don't know how to few the output?  I listed what the
 variable got stored in it, but I don't know how I can get 
 anything else out
 of it. Are there other ways to view the results?
 Also, I'm confused about the last line 6  variables and  8 
 observations
 Aren't the rows the variables and the columns the observations?
  
 (NOTE: if anyone knows a good guide for doing a PCA on an 
 example data set,
 from start to finish, it would be greatly appreciated)
  
  m
  [,1] [,2] [,3] [,4] [,5] [,6]
 [1,]123456
 [2,]343781
 [3,]123456
 [4,]874143
 [5,]123456
 [6,]458213
 [7,]987678
 [8,]137350
  pc.cr-princomp(m,cor=TRUE)
  pc.cr
 Call:
 princomp(x = m, cor = TRUE)
  
 Standard deviations:
   Comp.1   Comp.2   Comp.3   Comp.4   Comp.5
 Comp.6 
 1.545609e+00 1.407093e+00 9.886649e-01 7.539927e-01 2.919276e-01
 2.460515e-09 
  
  6  variables and  8 observations.
 
  
 thanks! 
 Dave 
 
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