View source: R/hierarchicalFWER.R

hierarchicalFWER | R Documentation |

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

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

`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: |

`Shaffer` |
boolean, if TRUE, a Shaffer correction is performed |

`addRoot` |
If TRUE, add a common root containing all the groups |

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

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.

Meinshausen, Nicolai. "Hierarchical Testing of Variable Importance." Biometrika 95.2 (2008): 265-78.

selFWER, hierarchicalFDR

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 <- hierarchicalFWER(X, y, res$group[[20]], res$var[[20]])

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