Thanks Matt.

 

Can you please provide code to work out the PCA in each imputed dataset???

Actually I went through and did this by hand in all 64 imputed datasets (for 50% missing data) – and then the code for analyzing it would not work at all….

Extremely frustrating….

 

I tried this with missMDA and factoMineR and PCA – but it only gave one dataset at the end and the results were not robust….

 

But I really liked the Amelia framework and wanted to use it – but could not make the code run after constructing PCA’s in each dataset as noted earlier.

 

Thanks for your advice,

 

Stuart.

 

 

 

 

 

 

 

From: Matt Blackwell [mailto:mblackwell@gov.harvard.edu]
Sent: Tuesday, 13 October 2020 11:59 AM
To: stuart.reece@bigpond.com
Cc: amelia@lists.gking.harvard.edu; Gary King; James Honaker; Stuart Reece
Subject: Re: Principal Components of Amelia Datasets

 

Hi Stuart, 

 

Probably the most straightforward way to do this would be to apply PCA to each of the imputed data sets and then use those in whatever analysis models you want. As an alternative, you could use the stacked dataset of all imputation (see my earlier email) and run PCA giving each of the rows of the stacked data (1/m) weight where m is the number of imputed datasets. This would ensure that all of the imputed data sets use the same factor loadings. 

 

Cheers,
Matt

 

~~~~~~~~~~~

Matthew Blackwell

Associate Professor of Government

Harvard University

url: http://www.mattblackwell.org

 

 

On Fri, Oct 9, 2020 at 10:38 PM <stuart.reece@bigpond.com> wrote:

Hi Amelia Users.

 

I was wondering if anyone would advise how I can add principal components to imputed datasets – and how to correctly combine them from all the imputations ???

 

I was not able to find anything on this online….

 

Thanks so much,

 

Stuart Reece.