[R] princomp() with missing values in panel data?

[Prof Brian Ripley]

See ?na.exclude (on the same page as na.omit)

On Mon, 16 Jan 2006, ivo welch wrote:

> dear R wizards:  the good news is that I know how to omit missing
> observations and run a principal components analysis.
>
>  p= princomp( na.omit( dataset ) )
>  p$scores[ ,1]  # the first factor
>
> (where dataset contains missing values;  incidentally, princomp(retailsmall,
> na.action=na.omit) does not work for me, so I must be doing something wrong,
> here.)

See ?princomp: only the formula method has an na.action argument.

> the bad news is that I would like NA observations to be retained as
> NA, so that I can reinsert the factors into the data set:
>  dataset$first.factor = p$scores[,1]
> there must be an elegant way of doing this.  help appreciated.
>
> may I humbly suggest that in linear models, it would be intuitive if the
> default would be for NA's to be ignored in the model computations, and that
> the functions residuals and fitted (and similar, such as scores() ) to
> understand when a particular obs num should be NA?

There is no function scores().

> help, as always, appreciated.
>
> sincerely,
>
> /ivo welch
>
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>
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-- 
Brian D. Ripley,                  ripley at stats.ox.ac.uk
Professor of Applied Statistics,  http://www.stats.ox.ac.uk/~ripley/
University of Oxford,             Tel:  +44 1865 272861 (self)
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