fullProcess: Full process of MLGL

In MLGL: Multi-Layer Group-Lasso

Description

Run hierarchical clustering following by a group-lasso on all the different partition and a hierarchical testing procedure. Only for linear regression problem.

Usage

fullProcess(
  X,
  y,
  control = c("FWER", "FDR"),
  alpha = 0.05,
  test = partialFtest,
  hc = NULL,
  fractionSampleMLGL = 1/2,
  BHclust = 50,
  nCore = NULL,
  ...
)

fullProcess.formula(
  formula,
  data,
  control = c("FWER", "FDR"),
  alpha = 0.05,
  test = partialFtest,
  hc = NULL,
  fractionSampleMLGL = 1/2,
  ...
)

Arguments

X	matrix of size n*p
y	vector of size n.
control	either "FDR" or "FWER"
alpha	control level for testing procedure
test	test used in the testing procedure. Default is partialFtest
hc	output of hclust function. If not provided, hclust is run with ward.D2 method. User can also provide the desired method: "single", "complete", "average", "mcquitty", "ward.D", "ward.D2", "centroid", "median".
fractionSampleMLGL	a real between 0 and 1 : the fraction of individuals to use in the sample for MLGL (see Details).
BHclust	number of replicates for computing the distance matrix for the hierarchical clustering tree
nCore	number of cores used for distance computation. Use all cores by default.
...	Others parameters for MLGL
formula	an object of class "formula" (or one that can be coerced to that class): a symbolic description of the model to be fitted.
data	an optional data frame, list or environment (or object coercible by as.data.frame to a data frame) containing the variables in the model. If not found in data, the variables are taken from environment(formula)

Details

Divide the n individuals in two samples. Then the three following steps are done : 1) Hierarchical CLustering of the variables of X based on the first sample of individuals 2) MLGL on the second sample of individuals 3) Hierarchical testing procedure on the first sample of individuals.

Value

a list containing :

res

output of MLGL function

lambdaOpt

lambda values maximizing the number of rejects

var

A vector containing the index of selected variables for the first lambdaOpt value

group

A vector containing the values index of selected groups for the first lambdaOpt value

selectedGroups

Selected groups for the first lambdaOpt value

reject

Selected groups for all lambda values

alpha

Control level

test

Test used in the testing procedure

control

"FDR" or "FWER"

time

Elapsed time

Author(s)

Quentin Grimonprez

See Also

MLGL, hierarchicalFDR, hierarchicalFWER, selFDR, selFWER

Examples

# least square loss
set.seed(42)
X <- simuBlockGaussian(50, 12, 5, 0.7)
y <- X[,c(2,7,12)] %*% c(2,2,-2) + rnorm(50, 0, 0.5)
res <- fullProcess(X, y)

MLGL documentation built on Dec. 31, 2019, 3 a.m.