cv.tripls: Cross-validation for trilinear PLS regression

In SvenSerneels/tripls_r: Trilinear partial least squares regression

Description

Provides random subset k-fold cross validation for the tripls function. If plot=TRUE, a root mean squared error of prediction plot is drawn.

Usage

cv.tripls(X, y, Amax, scaling = TRUE, fold = 10, repeats = 100, plot = TRUE, colors = list(line = "#0000AA", background = "#CCCCFF"))

Arguments

X	The X data as a 3 way (n x p x q) array.
y	The corresponding y data as an n x 1 vector or a length n numeric
Amax	The maximal number of latent components to estimate (integer)
scaling	Logical flag. If TRUE, the data are internally centered and scaled to unit variance. If FALSE, the data are only centered.
fold	The number of cases to leave out in each random cross-validation repeat (integer)
repeats	The number of times to repeat the cross validation process (integer)
plot	Logical flag. If TRUE, an RMSEP plot vs. the number of components is plotted.
colors	A List object specifying the colors for the RMSEP plot. It contains the elements "lines" and "background".

Value

The function returns a list object containing

cvres	A data frame containing a vector of the number of components (1:Amax), and the corresponding RMSEC and RMSEP values
mincv	The number of components that corresponds to the minimal RMSEP

Author(s)

Sven Serneels, BASF Corp.

Examples

data(data_3)
cv.tripls(data_3$X3, data_3$y3, 3, scaling = TRUE, fold = 4, repeats = 100, 
    plot = TRUE, colors = list(line = "#0000AA", background = "#CCCCFF")) 

SvenSerneels/tripls_r documentation built on May 4, 2019, 6:30 a.m.