# cross validation for EMlasso

### Description

cross validation function for `EMlasso`

.

### Usage

1 2 3 |

### Arguments

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

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

`lambda` |
Values at which prediction error should be computed. |

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

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

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

`model` |
"linear" or "logistic". |

`burn` |
Number of steps for the burn period. |

`threshold` |
Zero tolerance. Coefficients under this value are set to zero. |

`eps` |
Tolerance of the EM algorithm. |

`epsCG` |
Epsilon for the convergence of the conjugate gradient. |

### Value

A list containing

- cv
Mean prediction error for each value of index.

- cvError
Standard error of

`lambda`

.- minCv
Minimal

`lambda`

criterion.- lambda
Values of

`lambda`

at which prediction error should be computed.- lambda.optimal
Value of

`lambda`

for which the cv criterion is minimal.

### Author(s)

Quentin Grimonprez, Serge Iovleff

### Examples

1 2 |

Want to suggest features or report bugs for rdrr.io? Use the GitHub issue tracker. Vote for new features on Trello.