Description Usage Arguments Details Value Author(s) References Examples

Fit a sequence of conditional logistic regression with lasso penalty, for small to large sized samples

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`X` |
Input matrix, of dimension nobs x nvars; each row is an observation vector |

`y` |
Binary response variable, with 1 for cases and 0 for controls |

`strata` |
Vector with stratum membership of each observation |

`fraction` |
Sequence of lambda values |

`nbfraction` |
The number of lambda values - default is 100 |

`nopenalize` |
List of coefficients not to penalize starting at 0 |

`BACK` |
If TRUE, use Backtracking-line search -default is TRUE |

`standardize` |
Logical flag for x variable standardization, prior to fitting the model sequence. |

`maxit` |
Maximum number of iterations of outer loop - default is 100 |

`maxitB` |
Maximum number of iterations in Backtracking-line search - default is 100 |

`thr` |
Threshold for convergence in lassoshooting. Default value is 1e-10. Iterations stop when max absolute parameter change is less than thr |

`tol` |
Threshold for convergence-default value is 1e-10 |

`epsilon` |
ratio of smallest to largest value of regularisation parameter at which we find parameter estimates |

`trace` |
If TRUE the algorithm will print out information as iterations proceed -default is TRUE |

`log` |
If TRUE, fraction are spaced uniformly on the log scale |

`adaptive` |
If TRUE adaptive lasso is fitted-default is FALSE |

`separate` |
If TRUE, the weights in adaptive lasso are build separately using univariate models. Default is FALSE, weights are build using multivariate model |

`ols` |
If TRUE, weights less than 1 in adaptive lasso are set to 1. Default is FALSE |

`p.fact` |
Weights for adaptive lasso |

`remove` |
If TRUE, invariable covariates are removed-default is FALSE |

The sequence of models implied by fraction is fit by IRLS (iteratively reweighted least squares) algorithm. by coordinate descent with warm starts and sequential strong rules

An object of type `clogitLasso`

which is a list with the following
components:

`beta` |
nbfraction-by-ncol matrix of estimated coefficients. First row has all 0s |

`fraction` |
A sequence of regularisation parameters at which we obtained the fits |

`nz` |
A vector of length nbfraction containing the number of nonzero parameter estimates for the fit at the corresponding regularisation parameter |

`arg` |
List of arguments |

Marta Avalos, Helene Pouyes, Marius Kwemou and Binbin Xu

Avalos, M., Pouyes, H., Grandvalet, Y., Orriols, L., & Lagarde, E. (2015). *Sparse conditional logistic
regression for analyzing large-scale matched data from epidemiological studies: a simple algorithm.* BMC bioinformatics, 16(6), S1. doi: 10.1186/1471-2105-16-S6-S1.

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```
fraction 40
fraction 80
```

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