okay, an extreme case, only 1 component, explains 100%, something weird
going on..
> 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
stephen sefick wrote:
> 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 <binab...@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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>>
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>>
>
>
>
>
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