Hi: I'm not familar with prcomp but with the principal components function
in bill revelle's psych package , one can specify the number of components
one wants to use to build the "closest" covariance matrix I don't know
what tol is doing in your example but it's not doing that.
                                     Â
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             mark
On Nov 9, 2009, zubin <binab...@bellsouth.net> wrote:
All 8 variables are still in the analysis, i am just reducing the number
of components being estimated i thought..
Example 1 component 8 variables, there is no way 1 component explains
100% of the variance of the 8 variable data set.
> 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 1.00
> summary(princ)
Rotation:
PC1
VIX0 -0.08217686
UUP0 -0.18881983
USO0 0.26647346
GLD0 0.26983923
HYG0 0.60674758
term0 0.18220237
spread0 0.61614047
TNX0 0.18111684
Daniel Malter wrote:
> In the first PCA you ask how much variance of the EIGHT (!) variables is
> captured by the first, second,..., eigth principal component.
>
> In the second PCA you ask how much variance of the THREE (!) variables
is
> captured by the first, second, and third principal component.
>
> Of course you need only as many PCs as there are variables to capture
100 %
> of the variance. Your "problem" thus comes from the fact that you have
eight
> variables in the first PCA, which requires eight PCs to capture 100%,
and
> that you have only three variables in the second PCA, which naturally
only
> requires three PCs to capture 100% of the variance.
>
> So it's more, yes, you are missing something in this case, rather than
that
> something is wrong with the analyses.
>
> HTH,
> Daniel
>
> -------------------------
> 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 PM
> An: [3]r-h...@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 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 when i go from total solution of 8 (8 variables in the data set), to
a
> reduced number of components, i can evaluate % of variance explained, or
am
> I missing something??
>
> 8 variables in the data set
>
> > princ = prcomp(df[,-1],rotate="varimax",scale=TRUE)
> > summary(princ)
> Importance of components:
> PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8
> Standard deviation 1.381 1.247 1.211 0.994 0.927 0.764 0.6708 0.4366
> Proportion of Variance 0.238 0.194 0.183 0.124 0.107 0.073 0.0562 0.0238
> Cumulative Proportion 0.238 0.433 0.616 0.740 0.847 0.920 0.9762
*1.0000*
>
> > princ = prcomp(df[,-1],rotate="varimax",scale=TRUE,tol=.75)
> > summary(princ)
>
> Importance of components:
> PC1 PC2 PC3
> Standard deviation 1.381 1.247 1.211
> Proportion of Variance 0.387 0.316 0.297 Cumulative Proportion 0.387
0.703
> *1.000*
>
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>
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