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

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

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | ```
fullProcess(
X,
y,
control = c("FWER", "FDR"),
alpha = 0.05,
test = partialFtest,
hc = NULL,
fractionSampleMLGL = 1/2,
BHclust = 50,
nCore = NULL,
addRoot = FALSE,
Shaffer = FALSE,
...
)
fullProcess.formula(
formula,
data,
control = c("FWER", "FDR"),
alpha = 0.05,
test = partialFtest,
hc = NULL,
fractionSampleMLGL = 1/2,
BHclust = 50,
nCore = NULL,
addRoot = FALSE,
Shaffer = FALSE,
...
)
``` |

`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 |

`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. |

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

`Shaffer` |
If TRUE, a Shaffer correction is performed (only if control = "FWER") |

`...` |
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) |

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

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

Quentin Grimonprez

MLGL, hierarchicalFDR, hierarchicalFWER, selFDR, selFWER

1 2 3 4 5 | ```
# 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)
``` |

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