Dear Katrien,
The sum of the squared singular values in PLS equals the sum of all the squared pairwise covariances between the two blocks of variables. Therefore, what you can compute is the fraction of "squared" total covariance that each PLS dimension accounts for. You can do this by dividing each squared singular value by the summed squared singular values. Also, if one insists on a significance test, it should be based on the distribution of these singular values. Best, Philipp Am Donnerstag, 14. Mai 2020 03:31:12 UTC+2 schrieb katrien.janin: > > Hello everybody, > > I am hoping you can help me figure out the following: after having used > the integration.test function (geomorph), I would like to know the % each > PLS captures of the total covariance but I am kinda stumped on what might > be a good way how to go about it. > > I was initially thinking along the lines of generating the covariance > matrix of the two block (e.g. cov.il.is <-cov(il.is.int$A1.matrix, > il.is.int$A2.matrix) and computing the eigenvalue for each PLS , and then > regress these. But of course the cov matrix has a very different structure, > so that will not work. I am clearly thinking in the wrong direction, but > for now I can’t seem to see the tree in the forest. Any ideas? > > Best wishes, > Katrien > -- You received this message because you are subscribed to the Google Groups "Morphmet" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. To view this discussion on the web visit https://groups.google.com/d/msgid/morphmet2/76c0b670-b6ed-448a-b80d-4d662263aa83%40googlegroups.com.
