MLGL (version 1.0.0)

hierarchicalFDR: Hierarchical testing with FDR control

Description

Apply hierarchical test for each hierarchy, and test external variables for FDR control at level alpha

Usage

hierarchicalFDR(X, y, group, var, test = partialFtest, addRoot = FALSE)

Value

a list containing:

pvalues

pvalues of the different test (without correction)

adjPvalues

adjusted pvalues

groupId

Index of the group

hierMatrix

Matrix describing the hierarchical tree.

Arguments

X

original data

y

associated response

group

vector with index of groups. group[i] contains the index of the group of the variable var[i].

var

vector with the variables contained in each group. group[i] contains the index of the group of the variable var[i].

test

function for testing the nullity of a group of coefficients in linear regression. The function has 3 arguments: X, the design matrix, y, response, and varToTest, a vector containing the indices of the variables to test. The function returns a p-value

addRoot

If TRUE, add a common root containing all the groups

Details

Version of the hierarchical testing procedure of Yekutieli for MLGL output. You can use th selFDR function to select groups at a desired level alpha.

References

Yekutieli, Daniel. "Hierarchical False Discovery Rate-Controlling Methodology." Journal of the American Statistical Association 103.481 (2008): 309-16.

See Also

selFDR, hierarchicalFWER

Examples

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 <- MLGL(X, y)
test <- hierarchicalFDR(X, y, res$group[[20]], res$var[[20]])