after PCA, the pc values are so large, wrong?
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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