Hi,

On Wed, Feb 29, 2012 at 9:52 AM, Blaz Simcic <

[hidden email]> wrote:

> Dear R buddies,

> I’m trying to run Principal Component Analysis, package

> princomp:

http://stat.ethz.ch/R-manual/R-patched/library/stats/html/princomp.html.

I'm going to assume you actually mean the princomp() function.

> My question is: why do I get different results with pca =

> princomp (x, cor = TRUE) and pca = princomp (x, cor = FALSE) even when I

> standardize variables in my matrix?

Because you didn't use the standardization that's used in princomp, most likely,

but you don't include reproducible code so it's impossible to actually

answer your

question. Look at this for ideas, though. Using scale() is equivalent

to using cor=TRUE.

> data(iris)

> iris.pcaCOR <- princomp(iris[,1:4], cor=TRUE)

> iris.pcaSCALE <- princomp(scale(iris[,1:4]), cor=TRUE)

>

> summary(iris.pcaCOR)

Importance of components:

Comp.1 Comp.2 Comp.3 Comp.4

Standard deviation 1.7083611 0.9560494 0.38308860 0.143926497

Proportion of Variance 0.7296245 0.2285076 0.03668922 0.005178709

Cumulative Proportion 0.7296245 0.9581321 0.99482129 1.000000000

> summary(iris.pcaSCALE)

Importance of components:

Comp.1 Comp.2 Comp.3 Comp.4

Standard deviation 1.7083611 0.9560494 0.38308860 0.143926497

Proportion of Variance 0.7296245 0.2285076 0.03668922 0.005178709

Cumulative Proportion 0.7296245 0.9581321 0.99482129 1.000000000

--

Sarah Goslee

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