-Original Message-
I avoid the biplot at all costs, because IMHO it violates one
of the tenets of good graphic design: It has two entirely
different scales on axes. These are maximally confusing to
the end-user. So I never use it.
I think you're being unnecessarily
I think the question on your mind should be: 'what do I want to do with this
plot'? Just producing output from the PCA is easy - plotting the output$sd
is probably quite informative. From the sounds of it, though, you want to do
clustering with the PCA component loadings? (Since that's mostly what
[...]
But having indicated that I don't see a biplot's multiple scales as
particularly likely to confuse or mislead, I'm always interested in
alternatives. The interesting question is 'given the same objective - a
qualitative indication of which variables have most influenced the location
I have a decent sized matrix (36 x 11,000) that I have preformed a PCA on
with prcomp(), but due to the large number of variables I can't plot the
result with biplot(). How else can I plot the PCA output?
I tried posting this before, but got no responses so I'm trying again.
Surely this is a
That depends on what you want to plot there. Basically, you could just use
plot() with pcaResult$x. You might need to define which PCs you want to plot
there though.
pcaResult-prcomp(iris[,1:4])
plot(pcaResult$x) # gives the first 2 PCs
plot(pcaResult$x[,2:3]) #gives the second vs the 3rd PC
To add: If thats not it, maybe you could be a bit more specific about what you
consider the result, and how you want it visualized.
Am 07.05.2012 um 15:24 schrieb Jessica Streicher:
That depends on what you want to plot there. Basically, you could just use
plot() with pcaResult$x. You might
Christian, is that 36 samples x 11K variables? Sounds like it. Is this
spectroscopic data?
In any case, the scores are in the list element $x as follows:
answer - prcomp(your matrix)
answer$x contains the scores, so if you want to plot the 1st 2 pcs, you could do
plot(answer$x[,1],
Biplot, depending on what parameters you give it, scales the data in a certain
way.
See
http://stat.ethz.ch/R-manual/R-patched/library/stats/html/biplot.princomp.html
scale
The variables are scaled by lambda ^ scale and the observations are scaled by
lambda ^ (1-scale) where lambda are the
And i always forget the question..
I haven't understood biplots a 100%, but from what i gleaned this scaling is
done so it looks better/is easier to read, while the scaling retains certain
properties of the biplot (something about projecting).
If you want to use the data for anything else, i
I don't know the answer, Jessica gave some insight.
I avoid the biplot at all costs, because IMHO it violates one of the tenets of
good graphic design: It has two entirely different scales on axes. These are
maximally confusing to the end-user. So I never use it.
If it is gene expression
Hi Bryan,
Many thanks for the replies.
The data is gene expression data for 36 samples over 11k genes.
I see that I can plot PC1 vs PC2 by using $x, but compared to biplot() I
can see that the range of values are different. For example, if I use
plot() the PC1 scale ranges from -150 to 150
Hi Jessica,
THanks for pointing that out. The scaling in biplot() doesn't seem to make
sense to me, however. The default value for scale=1 therefore lambda ^
(1-scale) - lambda ^ 0 which is 1 regardless of what lambda is. Which
can't be right?
Anyway, I won't worry about it anymore as you and
Hi Jessica,
Yes, that does help. It confirms my digging around in the prcomp object.
I was plotting $x, but wasn't sure whether this was appropriate. Mainly
because the data ranges are different in $x than when plotted by biplot()
- as I mentioned my reply to Bryan. Do you know if this
Hi Bryan,
On 07/05/2012 15:33, Bryan Hanson han...@depauw.edu wrote:
I don't know the answer, Jessica gave some insight.
I avoid the biplot at all costs, because IMHO it violates one of the
tenets of good graphic design: It has two entirely different scales on
axes. These are maximally
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