cv.OHPL: Cross-Validation for Ordered Homogeneity Pursuit Lasso

In OHPL: Ordered Homogeneity Pursuit Lasso for Group Variable Selection

Description

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
## Not run: 
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

## End(Not run)

OHPL documentation built on May 18, 2019, 9:03 a.m.