Hi,
I am a newbie at statistics and am trying to learn about principal
component (PC) analysis by reading and doing some examples with my
own data. I am using S-Plus (6.2 for Windows) for my analyses.
I have a dataframe (I think that's the right terminology) "mydat"
with various "columns" x1,x2,x3,x4,x5,x6 for which I am trying to
compute the principal components.
I use the following command to carry out the analysis:
princomp(x = ~ x1 + x2 + x3 +x4 + x5 + x6, data = mydat, scores =
T, cor = T, na.action = na.exclude)
From looking at output, I can get an idea of which PCs are
explaining a certain percent of the variance. I can also get
S-plus to predict the PCs for each "row" in the dataframe.
My question is "How are the individual PCs computed from the base
properties?" I can look at the loadings and I think these are
indicative of which properties contribute to which PCs, but I
don't know how to use this information to compute the actual values.
Are the PCs computed as a linear combination of the properties, e.g.
PC1 = a x1 + b x2 + c x3 + d x4 + e x5 + f x6 ?
If so, how do I get the coefficients?
Thanks for any assistance or advice you can provide.
-g
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