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Thanks for the great package! It is not clear to me how to select the number of PCA inside the function "widely_svd". Can I know the variability explained by individual PCA (i.e., selecting the optimal nv) ? This would justify selecting the number of PCAs. For now, I'm setting it to 100 (nv = 100) following the suggestion in "Supervised Machine Learning for Text Analysis in R" by Emil Hvitfeldt and Julia Silge.
Thank you very much!
The text was updated successfully, but these errors were encountered:
Let's think through how we might return other, more complete info from the SVD in the tidy format we use in this package. In the meantime, I would recommend that you use a lower-level interface to SVD like irlba so you can get out all the information you want.
Hello,
Thanks for the great package! It is not clear to me how to select the number of PCA inside the function "widely_svd". Can I know the variability explained by individual PCA (i.e., selecting the optimal nv) ? This would justify selecting the number of PCAs. For now, I'm setting it to 100 (nv = 100) following the suggestion in "Supervised Machine Learning for Text Analysis in R" by Emil Hvitfeldt and Julia Silge.
Thank you very much!
The text was updated successfully, but these errors were encountered: