Hello folks,
it may be regarded as a user error to scale() your data prior to prcomp()
instead of using its 'scale.' argument. However, it is a user thing that may
happen and sounds a legitimate thing to do, but in that case predict() with
'newdata' can give wrong results:
x <- scale(USArrests)
sol <- prcomp(x)
all.equal(predict(sol), predict(sol, newdata=x))
## [1] "Mean relative difference: 0.9033485"
Predicting with the same data gives different results than the original PCA of
the data.
The reason of this behaviour seems to be in these first lines of
stats:::prcomp.default():
x <- scale(x, center = center, scale = scale.)
cen <- attr(x, "scaled:center")
sc <- attr(x, "scaled:scale")
If input data 'x' have 'scaled:scale' attribute, it will be retained if scale()
is called with argument "scale = FALSE" like is the case with default options
in prcomp(). So scale(scale(x, scale = TRUE), scale = FALSE) will have the
'scaled:center' of the outer scale() (i.e, numerical zero), but the
'scaled:scale' of the inner scale().
Function princomp finds the 'scale' directly instead of looking at the
attributes of the input data, and works like expected:
sol <- princomp(x)
all.equal(predict(sol), predict(sol, newdata=x))
## [1] TRUE
I don't have any nifty solution to this -- only checking the 'scale.' attribute
and acting accordingly:
sc <- if (scale.) attr(x, "scaled:scale") else FALSE
Cheers, Jari Oksanen
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