mvdaloo: Leave-one-out routine for 'mvdareg' objects

In mvdalab: Multivariate Data Analysis Laboratory

Leave-one-out routine for mvdareg objects

Description

When validation = 'loo' this routine effects the leave-one-out cross-validation procedure for mvdareg objects.

Usage

mvdaloo(X, Y, ncomp, weights = NULL, method = "bidiagpls", 
        scale = FALSE, boots = NULL, ...)

Arguments

X	a matrix of observations. NAs and Infs are not allowed.
Y	a vector. NAs and Infs are not allowed.
ncomp	the number of components to include in the model (see below).
weights	currently not in use
method	PLS algorithm used
scale	scaling used
boots	not applicable for validation = 'loo'
...	additional arguments. Currently ignored.

Details

This function should not be called directly, but through the generic function plsFit with the argument validation = 'loo'.

Value

Provides the following bootstrapped results as a list for mvdareg objects:

cvR2	leave-one-out estimate of cvR2.
PRESS	leave-one-out estimate of prediction error sums of squares.
MSPRESS	leave-one-out estimate of mean squared error prediction sums of squares.
RMSPRESS	leave-one-out estimate of mean squared error prediction sums of squares.
in.bag	leave-one-out samples used for model building.

Author(s)

Nelson Lee Afanador ([e-mail]), Thanh Tran ([e-mail])

References

NOTE: This function is adapted from mvr in package pls with extensive modifications by Nelson Lee Afanador and Thanh Tran.

See Also

plsFit, mvdaboot

Examples

data(Penta)
mod1 <- plsFit(log.RAI ~., scale = TRUE, data = Penta[, -1], 
        ncomp = 2, method = "bidiagpls", validation = "loo")

mod1$validation$cvR2
mod1$validation$PRESS
mod1$validation$MSPRESS
mod1$validation$RMSPRESS
mod1$validation$in.bag  

mvdalab documentation built on Oct. 6, 2022, 1:05 a.m.