loo_internal {xnet}

Leave-one-out cross-validation for two-step kernel ridge regression

Description

These functions implement different cross-validation scenarios for two-step kernel ridge regression. It uses the shortcuts for leave-one-out cross-validation.

Usage

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)

Arguments

Y

the matrix with responses

Hk

the hat matrix for the first kernel (rows of Y)

Hg

the hat matrix for the second kernel (columns of Y)

pred

the predictions

...

added to allow for specifying pred even when not needed.

alpha

a vector of length 4 with the alpha values from a linearFilter model

Details

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.

Value

a matrix with the leave-one-out predictions

See Also

loo for the user-level function.