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

cv.MLGL

R Documentation

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

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.