cv.MLGL: Multi-Layer Group-Lasso with cross V-fold validation

In MLGL: Multi-Layer Group-Lasso

Multi-Layer Group-Lasso with cross V-fold validation

Description

V-fold cross validation for MLGL function

Usage

cv.MLGL(
  X,
  y,
  nfolds = 5,
  lambda = NULL,
  hc = NULL,
  weightLevel = NULL,
  weightSizeGroup = NULL,
  loss = c("ls", "logit"),
  intercept = TRUE,
  sizeMaxGroup = NULL,
  verbose = FALSE,
  ...
)

Arguments

X	matrix of size n*p
y	vector of size n. If loss = "logit", elements of y must be in -1,1
nfolds	number of folds
lambda	lambda values for group lasso. If not provided, the function generates its own values of lambda
hc	output of hclust function. If not provided, hclust is run with ward.D2 method
weightLevel	a vector of size p for each level of the hierarchy. A zero indicates that the level will be ignored. If not provided, use 1/(height between 2 successive levels)
weightSizeGroup	a vector
loss	a character string specifying the loss function to use, valid options are: "ls" least squares loss (regression) and "logit" logistic loss (classification)
intercept	should an intercept be included in the model ?
sizeMaxGroup	maximum size of selected groups. If NULL, no restriction
verbose	print some informations
...	Others parameters for cv.gglasso function

Details

Hierarchical clustering is performed with all the variables. Then, the partitions from the different levels of the hierarchy are used in the different run of MLGL for cross validation.

Value

a cv.MLGL object containing:

lambda

values of lambda.

cvm

the mean cross-validated error.

cvsd

estimate of standard error of cvm

cvupper

upper curve = cvm+cvsd

cvlower

lower curve = cvm-cvsd

lambda.min

The optimal value of lambda that gives minimum cross validation error cvm.

lambda.1se

The largest value of lambda such that error is within 1 standard error of the minimum.

time

computation time

Author(s)

Quentin Grimonprez

See Also

MLGL, stability.MLGL, predict.cv.gglasso, coef.cv.MLGL, plot.cv.MLGL

Examples

set.seed(42)
# Simulate gaussian data with block-diagonal variance matrix containing 12 blocks of size 5
X <- simuBlockGaussian(50, 12, 5, 0.7)
# Generate a response variable
y <- X[, c(2, 7, 12)] %*% c(2, 2, -2) + rnorm(50, 0, 0.5)
# Apply cv.MLGL method
res <- cv.MLGL(X, y)

MLGL documentation built on March 31, 2023, 9:32 p.m.