Hello,
I have a data set with 15 variables (first one is the response) and
1200 observations. Now I use pls package to do the plsr as below.
trainSet = as.data.frame(scale(trainSet, center = T, scale = T))
trainSet.plsr = mvr(formula, ncomp = 14, data = trainSet, method = "kernelpls",
model = TRUE, x = TRUE, y = TRUE)
from the model, I wish to know the values of Xvar (the amount of
X-variance explained by each number of components) and Xtotvar (total
variance in X).
Because the trainSet has been scaled before training, I think Xtotvar
should be equal to 14, but unexpectedly Xtotvar = 16562, and the
values of Xvar are also very big and sum of Xvar = 16562. Why does
this type of result occur? for the reason of kernel algorithm?
Thank you,
Shengzhe
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