modreg.control {dirttee} | R Documentation |

`modreg`

.This is an internal function of package `dirttee`

which allows control of the numerical options
for fitting mode regression. Typically, users will want to modify the defaults if model fitting
is slow or fails to converge.

```
modreg.control(
StartInterval = sqrt(3),
nStart = 11,
nInterim = NULL,
maxit = 100,
itInterim = 10,
tol = 10^-4,
tol_bw_plugin = 10^-3,
maxit_bw_plugin = 10,
maxit_penalty_plugin = 10,
tol_penalty_plugin = 10^-3,
tol_regopt = tol * 100,
tol_opt = 10^-3,
maxit_opt = 200,
tol_opt2 = 10^-3,
maxit_opt2 = 200
)
```

`StartInterval` |
Starting values are based on an estimate for the mean and an interval around it. The interval is |

`nStart` |
Number of starting values, considered in the first iteration. Default is 11. |

`nInterim` |
Probably has little impact on speed and result. After |

`maxit` |
Maximum number of iterations for the weighted least squares algorithm. Default is 100. |

`itInterim` |
Probably has little impact on speed and result. After |

`tol` |
Convergence criterion for the weighted least squares algorithm. Default is 10^-4. |

`tol_bw_plugin` |
Convergence criterion for bandwidth selection in the |

`maxit_bw_plugin` |
Maximum number of iterations for bandwidth selection in the |

`maxit_penalty_plugin` |
Maximum number of iterations for penalty selection in the |

`tol_penalty_plugin` |
Convergence criterion for penalty selection in the |

`tol_regopt` |
Weighted least squares are recalculated for hyperparameter optimization. This is the convergence criterion within this optimization. Default is |

`tol_opt` |
Convergence criterion for the first hyperparameter optimizion. Can be increased to reduce compuation time. Default is 10^-3. |

`maxit_opt` |
Maximum number of iterations for the first hyperparameter optimizion. Can be lowered to reduce compuation time. Default is 200. |

`tol_opt2` |
Convergence criterion for the second hyperparameter optimizion. Default is 10^-3. |

`maxit_opt2` |
Maximum number of iterations for the second hyperparameter optimizion. Default is 200. |

The algorithm is described in Seipp et al. (2022). To increase the speed of the algorithm, adapting `tol`

and `maxit_opt`

/`maxit_opt2`

and other penalty / hyperparameter optimization parameters are a good starting point.

A list with the arguments as components

Seipp, A., Uslar, V., Weyhe, D., Timmer, A., & Otto-Sobotka, F. (2022). Flexible Semiparametric Mode Regression for Time-to-Event Data. Manuscript submitted for publication.

Yao, W., & Li, L. (2014). A new regression model: modal linear regression. Scandinavian Journal of Statistics, 41(3), 656-671.

[Package *dirttee* version 1.0.1 Index]