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

V-fold cross validation for `MLGL`

function

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`X` |
matrix of size n*p |

`y` |
vector of size n. If loss = "logit", elements of y must be in -1,1 |

`nfolds` |
number of folds |

`lambda` |
lambda values for group lasso. If not provided, the function generates its own values of lambda |

`hc` |
output of |

`weightLevel` |
a vector of size p for each level of the hierarchy. A zero indicates that the level will be ignored. If not provided, use 1/(height between 2 successive levels) |

`weightSizeGroup` |
a vector |

`loss` |
a character string specifying the loss function to use, valid options are: "ls" least squares loss (regression) and "logit" logistic loss (classification) |

`intercept` |
should an intercept be included in the model ? |

`sizeMaxGroup` |
maximum size of selected groups. If NULL, no restriction |

`verbose` |
print some informations |

`...` |
Others parameters for |

Hierarchical clustering is performed with all the variables. Then, the partitions from the different levels of the hierarchy are used in the different run of MLGL for cross validation.

a cv.MLGL object containing:

- lambda
values of

`lambda`

.- cvm
the mean cross-validated error.

- cvsd
estimate of standard error of

`cvm`

- cvupper
upper curve =

`cvm+cvsd`

- cvlower
lower curve =

`cvm-cvsd`

- lambda.min
The optimal value of

`lambda`

that gives minimum cross validation error`cvm`

.- lambda.1se
The largest value of

`lambda`

such that error is within 1 standard error of the minimum.- time
computation time

Quentin Grimonprez

MLGL, stability.MLGL, predict.cv.gglasso, coef.cv.MLGL, plot.cv.MLGL

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