Hello, I have a dataset with many variables and I'd like to do dimensionality reduction with Independent Component Analysis. There are many statistical methods to estimate the latent variables of the ICA model. I'm trying the R package "proDenICA" that implements the penalized maximum likelihood method proposed by Hastie, Tibshirani and Friedman in Section 14.7.4 of the book "Elements of Statistical Learning". The documentation of the proDenICA function says that the argument "k" is the "Number of components required, less than or equal to the number of columns of x". If I choose a value of k less than the number of colomns of x, I get an error message. It seems to me that I'm not using the function proDenICA() as it is meant to be used. Am I missing something?
I've reproduced the problem with a smaller dataset here: > library(MASS) > data(crabs) > str(crabs) 'data.frame': 200 obs. of 8 variables: $ sp : Factor w/ 2 levels "B","O": 1 1 1 1 1 1 1 1 1 1 ... $ sex : Factor w/ 2 levels "F","M": 2 2 2 2 2 2 2 2 2 2 ... $ index: int 1 2 3 4 5 6 7 8 9 10 ... $ FL : num 8.1 8.8 9.2 9.6 9.8 10.8 11.1 11.6 11.8 11.8 ... $ RW : num 6.7 7.7 7.8 7.9 8 9 9.9 9.1 9.6 10.5 ... $ CL : num 16.1 18.1 19 20.1 20.3 23 23.8 24.5 24.2 25.2 ... $ CW : num 19 20.8 22.4 23.1 23 26.5 27.1 28.4 27.8 29.3 ... $ BD : num 7 7.4 7.7 8.2 8.2 9.8 9.8 10.4 9.7 10.3 ... > X=crabs[,4:8] > X=as.matrix(X) > library(ProDenICA) > out.proDen = ProDenICA(X, k = 2, whiten = TRUE, maxit = 20, trace=T) Error in solve.default(V, W) : 'a' (5 x 2) must be square I get the error with k = 1,2,3,4. The function works with k=5. Thank you. ______________________________________________ R-help@r-project.org 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.
