cross validation function for `EMlasso`

.

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`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. |

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.

Quentin Grimonprez, Serge Iovleff

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