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03-08-2017 10:06 PM

Hi,

I have been using proc princomp to perform a principal components analysis. It gave me the eigen vectors, but not the principal components of the original data. Is it possible to transform the original data in order to get the princpal components and later use them in a regression?

Thank you

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Solution

03-11-2017
07:19 PM

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03-09-2017 05:42 AM

If you look at the Getting Started example in the PROC PRINCOMP documentation, you will see that

the eigenvectors are listed in the "Eigenvectors" table. They are named Prin1, Prin2, Prin3, etc.

You can use the OUTSTAT= option on the PROC PRINCOMP statement to create an output data set that contains the eigenvectors.

They are listed as the rows that are labeled PRIN1, PRIN2, etc.

If you want to use the PCs in a regression, I believe you want to use the SCORES, which are available in the OUT= data set.

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03-08-2017 11:01 PM

What does your code look like?

Can we assume you've tried the standard output datasets and they don't have the values?

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03-08-2017 11:28 PM

Check this out. http://blogs.sas.com/content/iml/2014/11/07/distribution-of-blood-types.html

Solution

03-11-2017
07:19 PM

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03-09-2017 05:42 AM

If you look at the Getting Started example in the PROC PRINCOMP documentation, you will see that

the eigenvectors are listed in the "Eigenvectors" table. They are named Prin1, Prin2, Prin3, etc.

You can use the OUTSTAT= option on the PROC PRINCOMP statement to create an output data set that contains the eigenvectors.

They are listed as the rows that are labeled PRIN1, PRIN2, etc.

If you want to use the PCs in a regression, I believe you want to use the SCORES, which are available in the OUT= data set.

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03-09-2017 10:32 PM

Thanks Rick, when I added "out = new_data" I obtained a data table which contains the original variables and the newly calculated principal components.

Could I now use these Principal Components in a regression? I have a IML code which does the Principal Components Analysis, but for some reason standardizes the Principal Components - but the PC's that I obtained above are not standardized, so I just want to make sure that these non standardized PCs are the ones that I should use in a regression.

Thanks