[R] prcomp - principal components in R

[stephen sefick]

principal components is  a data reduction technique.  It looks like
you have three axes that account for 100%.  Make this reporducible.

On Mon, Nov 9, 2009 at 11:37 AM, zubin <binabina at bellsouth.net> wrote:
> 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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-- 
Stephen Sefick

Let's not spend our time and resources thinking about things that are
so little or so large that all they really do for us is puff us up and
make us feel like gods.  We are mammals, and have not exhausted the
annoying little problems of being mammals.

								-K. Mullis