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]] > > ______________________________________________ [email protected] mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
