# cross validation

### Description

cross validation function for lars algorithm

### Usage

1 2 3 |

### Arguments

`X` |
the matrix (of size n*p) of the covariates. |

`y` |
a vector of length n with the response. |

`nbFolds` |
the number of folds for the cross-validation. |

`index` |
Values at which prediction error should be computed. When mode = "fraction", this is the fraction of the saturated |beta|. The default value is seq(0,1,by=0.01). When mode="lambda", this is values of lambda. |

`mode` |
Either "fraction" or "lambda". Type of values containing in partition. |

`maxSteps` |
Maximal number of steps for lars algorithm. |

`partition` |
partition in nbFolds folds of y. Must be a vector of same size than y containing the index of folds. |

`intercept` |
If TRUE, there is an intercept in the model. |

`eps` |
Tolerance of the algorithm. |

### Value

A list containing

- cv
Mean prediction error for each value of index.

- cvError
Standard error of cv.

- minCv
Minimal cv criterion.

- minIndex
Value of index for which the cv criterion is minimal.

- index
Values at which prediction error should be computed. This is the fraction of the saturated |beta|. The default value is seq(0,1,by=0.01).

- maxSteps
Maximum number of steps of the lars algorithm.

### Author(s)

Quentin Grimonprez

### Examples

1 2 |

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