These functions implement different cross-validation scenarios for two-step kernel ridge regression. It uses the shortcuts for leave-one-out cross-validation.
loo.i(Y, Hk, Hg, pred)loo.i0(Y, Hk, Hg, pred)
loo.r(Y, Hk, Hg, ...)
loo.c(Y, Hk, Hg, ...)
loo.b(Y, Hk, Hg, ...)
loo.e.sym(Y, Hk, pred)
loo.e.skew(Y, Hk, pred)
loo.e0.sym(Y, Hk, pred)
loo.e0.skew(Y, Hk, pred)
loo.v(Y, Hk, ...)
loo.i.lf(Y, alpha, pred)
loo.i0.lf(Y, alpha, pred)
the matrix with responses
the hat matrix for the first kernel (rows of Y)
the hat matrix for the second kernel (columns of Y)
the predictions
added to allow for specifying pred even when not needed.
a vector of length 4 with the alpha values from a
linearFilter model
a matrix with the leave-one-out predictions
These functions are primarily for internal use and hence not exported. Be careful when using them, as they do not perform any sanity check on the input. It is up to the user to make sure the input makes sense.
loo for the user-level function.