[R] princomp

[Kolling Alfons, F+E]

Hello experts,

as newcomer in pca, i have a question, concerning the princomp algorithm.
With a dataset "r" containing 18 "input" parameters and 1 "output" parameter
r[19], i got with the following fit

	ls <- lsfit(r[1:18],r[19]); lsdiag <- ls.diag(ls); lsdiag$std.dev

a prediction error of:
	[1] 8.879561  

what is quite reasonable. If i take only two significant important inputs, 
  
	ls <- lsfit(r[1:2],r[19]); lsdiag <- ls.diag(ls); lsdiag$std.dev

i will get an prediction error of:
	[1] 20.18148   

what is not so bad for only two of 18 input parameters. If i made an lsfit
with the scores of:

	p <- princomp(r[1:18],cor=TRUE)
	ls <- lsfit(p$scores[,1:18],r[19]); lsdiag <- ls.diag(ls);
lsdiag$std.dev
i got the reasonable error of:
	[1] 8.879561
(see above the first fit)
But (and here comes the question) if take the two most important principal
components for the lsfit

	ls <- lsfit(p$scores[,1:2],r[19]); lsdiag <- ls.diag(ls);
lsdiag$std.dev
i have an prediction error of:
	[1] 33.22741 

which is a good deal worse, compared to the 20.18148 from above. So what is
wrong? I thought, that the first principle components are the "most
important"?



mit freundlichem Gruß

Alfons Kolling


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