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 Eigenvaluesââ: 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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