Description Usage Arguments Details Value Author(s) References Examples

HiGLASSO is a regularization based selection method designed to detect non-linear interactions between variables, particularly exposures in environmental health studies.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 |

`Y` |
A length n numeric response vector |

`X` |
A n x p numeric matrix of covariates to basis expand |

`Z` |
A n x m numeric matrix of non basis expanded and non regularized covariates |

`method` |
Type of initialization to use. Possible choices are |

`lambda1` |
A numeric vector of main effect penalties on which to tune
By default, |

`lambda2` |
A numeric vector of interaction effects penalties on which to
tune. By default, |

`nlambda1` |
The number of lambda1 values to generate. Default is 10,
minimum is 2. If |

`nlambda2` |
The number of lambda2 values to generate. Default is 10,
minimum is 2. If |

`lambda.min.ratio` |
Ratio that calculates min lambda from max lambda. Ignored if 'lambda1' or 'lambda2' is non NULL. Default is 0.05 |

`sigma` |
Scale parameter for integrative weights. Technically a third tuning parameter but defaults to 1 for computational tractability |

`degree` |
Degree of |

`maxit` |
Maximum number of iterations. Default is 5000 |

`tol` |
Tolerance for convergence. Default is 1e-5 |

There are a few things to keep in mind when using `higlasso`

`higlasso`

uses the strong heredity principle. That is,`X_1`

and`X_2`

must included as main effects before the interaction`X_1 X_2`

can be included.While

`higlasso`

uses integrative weights to help with estimation,`higlasso`

is more of a selection method. As a result,`higlasso`

does not output coefficient estimates, only which variables are selected.Simulation studies suggest that

`higlasso`

is a very conservative method when it comes to selecting interactions. That is,`higlasso`

has a low false positive rate and the identification of a nonlinear interaction is a good indicator that further investigation is worthwhile.`higlasso`

can be slow, so it may may be beneficial to tweak some of its settings (for example,`nlambda1`

and`nlambda2`

) to get a handle on how long the method will take before running the full model.

An object of type "higlasso" with 4 elements:

- lambda
An

`nlambda1 x nlambda2 x 2`

array containing each pair`(lambda1, lambda2)`

pair.- selected
An

`nlambda1 x nlambda2 x ncol(X)`

array containing higlasso's selections for each lambda pair.- df
The number of nonzero selections for each lambda pair.

- call
The call that generated the output.

Alexander Rix

TODO

1 2 3 4 5 6 7 8 9 10 11 | ```
library(higlasso)
X <- as.matrix(higlasso.df[, paste0("V", 1:10)])
Y <- higlasso.df$Y
Z <- matrix(1, nrow(X))
## Not run:
# This can take a bit of time
higlasso.fit <- higlasso(Y, X, Z)
## End(Not run)
``` |

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