[R] after PCA, the pc values are so large, wrong?

[Ben Bolker]

bbslover <dluthm <at> yeah.net> writes:

> 
[snip]

> the fit result below:
> Call:
> lm(formula = y ~ x1 + x2 + x3, data = pc)
> 
> Residuals:
>      Min       1Q   Median       3Q      Max 
> -1.29638 -0.47622  0.01059  0.49268  1.69335 
> 
> Coefficients:
>               Estimate Std. Error t value Pr(>|t|)    
> (Intercept)  5.613e+00  8.143e-02  68.932  < 2e-16 ***
> x1          -3.089e-05  5.150e-06  -5.998 8.58e-08 ***
> x2          -4.095e-05  3.448e-05  -1.188    0.239    
> x3          -8.106e-05  6.412e-05  -1.264    0.210    
> ---
> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 
> 
> Residual standard error: 0.691 on 68 degrees of freedom
> Multiple R-squared: 0.3644,     Adjusted R-squared: 0.3364 
> F-statistic: 12.99 on 3 and 68 DF,  p-value: 8.368e-07 
> 
> x2,x3 is not significance. by pricipal, after PCA, the pcs should
> significance, but my data is not, why? 

  Why is it necessary that the first few principal components
should have significant relationships with some other response
values?  The strength, and weakness, of PCA is that it is
calculated *without regard* to a response variable, so it
does not constitute "data snooping" ... 
  I may of course have misinterpreted your question, but at
a quick look, I don't see anything obviously wrong here.