Principal Component Analysis

Hi,

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
http://www.functionaldiversity.org