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From |
Nick Cox <njcoxstata@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: pca and predict--confusion about what it does |

Date |
Sat, 20 Oct 2012 21:56:18 +0100 |

If you want another mean, all you need to do is add it. No option really needed, therefore. Nick On Sat, Oct 20, 2012 at 8:17 PM, Israel Pearce <ra.frbsf@gmail.com> wrote: > Thank you. I can't seem to find any options on Stata that do not scale > the PC's to have mean 0. Do you know of an option that could allow for > this or is it not a feature in Stata? > > On Sat, Oct 20, 2012 at 11:02 AM, Nick Cox <njcoxstata@gmail.com> wrote: >> The first predicted variable is the first PC, and so on. It's >> conventional that PCs are scaled to have mean 0. >> >> The absence of a response or outcome variable is irrelevant here. If >> you're confused a good reason is because Stata is stretching the >> meaning of -predict- here to include these constructed variables. (A >> bad reason is not reading the documentation...) >> >> Nick >> >> On Sat, Oct 20, 2012 at 6:32 PM, Israel Pearce <ra.frbsf@gmail.com> wrote: >>> I am confused about the principal component scores one gets from pca >>> postestimation. Let's say I wanted the first principal component >>> scores from a set of explanatory variables. I could do a pca on my x >>> variables getting eigen values and vectors then use predict pc1 (a >>> newly created var), focusing only on the first eigenvector. However, >>> if one does not have a y variable what is the score actually giving us >>> for individual observations? What is it a predicted value for? Also, >>> in theory I do not see why the sum of these observations must add to 0 >>> but they are. If someone understands this I would greatly appreciate >>> an explanation. Thanks! * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

**References**:**st: pca and predict--confusion about what it does***From:*Israel Pearce <ra.frbsf@gmail.com>

**Re: st: pca and predict--confusion about what it does***From:*Nick Cox <njcoxstata@gmail.com>

**Re: st: pca and predict--confusion about what it does***From:*Israel Pearce <ra.frbsf@gmail.com>

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