Hi Lucia,
It is another problem with Homals on my own data. Have you ever got a
eigenvalue above one? Because in my analysis homals consistently gave me
very small eigenvalues(far below 1), I compared the eigenvalues in Homals
and Psych, there were different, Psych always gave me high eigenvalues. you
can find the code in attachment.
Thanks,
Zhaoju
在 2013年5月17日星期五 UTC+2下午2:16:00，l. calciano写道：
>
> 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
>
>
>
> [[alternative HTML version deleted]]
>
>
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