# hierarchicalFDR: Hierarchical testing with FDR control In MLGL: Multi-Layer Group-Lasso

 hierarchicalFDR R Documentation

## 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)
```

### 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.

### Value

a list containing:

pvalues

pvalues of the different test (without correction)

groupId

Index of the group

hierMatrix

Matrix describing the hierarchical tree.

### References

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

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[], res\$var[])
```

MLGL documentation built on May 25, 2022, 5:05 p.m.