Use cross-validation to help select the optimal number of variable groups and the value of gamma.

## Usage

cv.OHPL(
X.cal,
y.cal,
maxcomp,
gamma = seq(0.1, 0.9, 0.1),
X.test,
y.test,
cv.folds = 5L,
G = 30L,
type = c("max", "median"),
scale = TRUE,
pls.method = "simpls"
)

## Arguments

X.cal

Predictor matrix (training)

y.cal

Response matrix with one column (training)

maxcomp

Maximum number of components for PLS

gamma

A vector of the gamma sequence between (0, 1).

X.test

X.test Predictor matrix (test)

y.test

y.test Response matrix with one column (test)

cv.folds

Number of cross-validation folds

G

Maximum number of variable groups

type

Find the maximum absolute correlation ("max") or find the median of absolute correlation ("median"). Default is "max".

scale

Should the predictor matrix be scaled? Default is TRUE.

pls.method

Method for fitting the PLS model. Default is "simpls". See the details section in plsr for all possible options.

## Value

A list containing the optimal model, RMSEP, Q2, and other evaluation metrics. Also the optimal number of groups to use in group lasso.

## Examples

data("wheat")

X <- wheat$x y <- wheat$protein
n <- nrow(wheat$x) set.seed(1001) samp.idx <- sample(1L:n, round(n * 0.7)) X.cal <- X[samp.idx, ] y.cal <- y[samp.idx] X.test <- X[-samp.idx, ] y.test <- y[-samp.idx] # this could run a while if (FALSE) { cv.fit <- cv.OHPL( x, y, maxcomp = 6, gamma = seq(0.1, 0.9, 0.1), x.test, y.test, cv.folds = 5, G = 30, type = "max" ) # the optimal G and gamma cv.fit$opt.G
cv.fit\$opt.gamma
}