R help - Jul 2006 - PCA with not non-negative definite covariance

Am I correct to understand from the previous discussions on this topic (a
few years back) that if I have a matrix with missing values my PCA options
seem dismal if:

 

(1)     I don’t want to impute the missing values.

(2)     I don’t want to completely remove cases with missing values.

(3)     I do cov() with use=”pairwise.complete.obs”, as this produces
negative eigenvalues (which it has in my case!).

 

This seems like such a shame as I would like to use PCA to plot my
clustering results. Any wisdom?

 

Quin

 

 

 


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Hi , hi all,
> Am I correct to understand from the previous discussions on this topic (a
> few years back) that if I have a matrix with missing values my PCA options
> seem dismal if:
> (1)     I don?t want to impute the missing values.
> (2)     I don?t want to completely remove cases with missing values.
> (3)     I do cov() with use=?pairwise.complete.obs?, as this produces
> negative eigenvalues (which it has in my case!).
(4) Maybe you can use the Non-linear Iterative Partial Least Squares (NIPALS)
algorithm (intensively used in chemometry). S. Dray proposes a version of this
procedure at http://pbil.univ-lyon1.fr/R/additifs.html.


Hope this help :)


Pierre