Hello!

I'm
using the NLPCA to reduce the
dimensionality of nine variables
(4 nominal /
3 ordinal /2 numeric)
to obtain the object-scores to be used as dependent variable in a regression model.

I'm using the package homals (http://www.jstatsoft.org/v31/i04/paper).



The output is:

Call: homals (date = date, Ndim = 1, rank = 1, level = c ("numerical", rep ("ordinal", 3), "numerical",
rep ("nominal", 4), active = TRUE)

Loss: 0.0002050824

Eigenvalues​​: D1 0.0212



I'm having
the following questions:

1)
Is it best to consider Ndim = rank = 1 or Ndim = rank = max (rank)  to reduce the dimensionality of data?

2) Is there a command to automatically calculate
the proportion of variance explained
by the first component? Otherwise, how can I calculate it by hand?3) Is it necessary to standardize numeric variables before perfoming "homals"?



If anyone
has any thoughts for this, responses would be greatly appreciated.

Thanks.

Lucia

 		 	   		  
 		 	   		  
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