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

[bbslover]

ok,I understand your means, maybe PLS is better for my aim. but I have done
that, also bad. the most questions for me is how to select less variables
from the independent to fit dependent. GA maybe is good way, but I do not
learn it well.

Ben Bolker wrote:
> 
> 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.
> 
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> 

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