# EM algorithm for lasso penalty

### Description

EM algorithm for lasso penalty

### 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` |
a sequence of l1 penalty regularization term. If no sequence is provided, the function computes his own sequence. |

`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 before thresholding some variables to zero. |

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

`eps` |
Epsilon for the convergence of the EM algorithm. |

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

### Value

A list containing :

- step
Vector containing the number of steps of the algorithm for every

`lambda`

.- variable
List of vector of the same length as

`lambda`

. The i-th item contains the index of non-zero coefficients for the i-th`lambda`

value.- coefficient
List of vector of the same length as

`lambda`

. The i-th item contains the non-zero coefficients for the i-th`lambda`

value.- lambda
Vector containing the

`lambda`

values.- mu
Intercept.

### Author(s)

Quentin Grimonprez, Serge Iovleff

### See Also

`EMcvlasso`

### Examples

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