When I use the principal function, like in the following code, I get a nice table which gives all the standardized loadings, as well as a table with the eigenvalues and the proportion and cumulative proportion explained.
rotatedpca <- principal(PCFdataset, nfactors = 8, rotate = "varimax", scores = T)
I would like to export this output to an excel file (using WriteXLS), but I can only do that for dataframes, and rotatedpca is not a dataframe and cannot be coerced into one it seems. I am able to extract the standardized loadings by using the following code:
loadings<-as.data.frame(unclass(rotatedpca$loadings))
But I cannot figure out how to access the other information that normally displays when I simply call the principal function, in particular the eigenvalues and the proportion and cumulative variance explained. I tried rotatedcpa$values, but that returns what looks like the eigenvalues for all 12 original variables as factors without rotation, which I don't understand. And I haven't been able to figure out any way to even try to extract the variance explained values. How can I simply create a dataframe that looks like the R output I get below from the principal function, for example?
RC2 RC3 RC8 RC1 RC4 RC5 RC6 RC7
SS loadings 1.52 1.50 1.45 1.44 1.01 1.00 0.99 0.98
Proportion Var 0.13 0.12 0.12 0.12 0.08 0.08 0.08 0.08
Cumulative Var 0.13 0.25 0.37 0.49 0.58 0.66 0.74 0.82
Proportion Explained 0.15 0.15 0.15 0.15 0.10 0.10 0.10 0.10
Cumulative Proportion 0.15 0.31 0.45 0.60 0.70 0.80 0.90 1.00
Thanks for reading my post!
dput()
your data or some other data to make this reproducible? what package is principal in? i would checknames(unclass(rotatedpca))
orstr(rotatedpca)
– Scissionpsych
package, as noted in the post title. – Pacificationprint.psych
which is an absolute beast of a function. But if you sift through it long enough, so you should find it there somewhere. – Ocularpsych:::print.psych.fa
, where about midway down you'll see the code that prints out that last piece. – Ocular