Is it possible to recover column names in PCA?
As such, while it’s interesting to find out how much each column in original data contributed to the components of a post-PCA dataset, the notion of “recovering” column names is a little misleading, and certainly misled me for a long time.
Is there a match between post PCA and original columns?
The only situation where there would be a match between post-PCA and original columns would be if the number of principle components were set at the same number as columns in the original. However, there would be no point in using the same number of columns because the data would not have changed.
Can a PCA be applied to binary data?
Although a PCA applied on binary data would yield results comparable to those obtained from a Multiple Correspondence Analysis (factor scores and eigenvalues are linearly related), there are more appropriate techniques to deal with mixed data types, namely Multiple Factor Analysis for mixed data available in the FactoMineR R package ( AFDM () ).
When to not use principal component analysis ( PCA )?
You can also take the first principal components (like: top 95% variance). You are looking for maximum interpretability: do not use PCA unless your data is in a good shape afterwards.
How to change column names of a data frame in R?
The new recommended way to do this is to use the setNames function. See ?setNames. Since this creates a new copy of the data.frame, be sure to assign the result to the original data.frame, if that is your intention. Newer versions of R will give you warning if you use colnames in some of the ways suggested by earlier answers.
Is the post PCA array the same as data scaled?
In this case, post_pca_array has the same 150 rows of data as data_scaled, but data_scaled ’s four columns have been reduced from four to two. The critical point here is that the two columns – or components, to be terminologically consistent – of post_pca_array are not the two “best” columns of data_scaled.