fullProcess: Full process of HCgglasso

Description Usage Arguments Details Value Author(s) See Also Examples

View source: R/fullProcess.R

Description

Run hierarchical clustering following by a group-lasso on all the different partition and a hierarchical testing procedure.

Usage

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fullProcess(X, y, control = c("FWER", "FDR"), alpha = 0.05,
  test = partialFtest, hc = NULL, plot = TRUE, ...)

Arguments

X

matrix of size n*p

y

vector of size n

control

either "FDR" or "FWER"

alpha

control elvel 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

plot

If TRUE plot the number of groups selected before and after the testing procedure

...

Others parameters

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) HCgglasso on the second sample of individuals 3) Hierarchical testing procedure on the first sample of individuals.

Value

a list containing :

res

output of HCgglasso function

lambdaOpt

lambda values maximizing the number of rejects

var

A vector containing the index of selected variables for lambdaOpt

group

A vector containing the values index of selected groups for lambdaOpt

Author(s)

Quentin Grimonprez

See Also

HCgglasso, hierarchicalFDR, hierarchicalFWER, selFDR, selFWER

Examples

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## Not run: 
set.seed(42)
X <- simuBlockGaussian(50,12,5,0.7)
y <- drop(X[,c(2,7,12)]%*%c(2,2,-2)+rnorm(50,0,0.5))
res <- fullProcess(X, y)

## End(Not run)

HCgglasso documentation built on May 2, 2019, 4:54 p.m.