Calculates initial optimal bandwidth with respect to mean squared error using K-fold cross-validation.

1 | ```
optimize.mse.BW(data, t0,tau,h.grid=seq(.01,2,length=50), folds=3, reps=2)
``` |

`data` |
n by 5 matrix. A data matrix where the first column is XL = min(TL, C) where TL is the time of the long term event, C is the censoring time, and the second column is DL =1*(TL<C), the third column is XS = min(TS, C) where TS is the time of the short term event, C is the censoring time, the fourth column is DS =1*(TS<C), and the fifth column is the covariate. These are the data used to calculate the estimated probability. |

`t0` |
the landmark time. |

`tau` |
the residual survival time of interest. |

`h.grid` |
The grid of possible bandwidths that the user would like the function to search through. Default is h.grid = seq(.01,2,length=50). |

`folds` |
Number of folds wanted for K-fold cross-validation. Default is 3. |

`reps` |
Number of repitions wanted of K-fold cross-validation. Default is 2. |

`h` |
Selected bandwidth. |

Layla Parast

Parast, Layla, Su-Chun Cheng, and Tianxi Cai. Incorporating short-term outcome information to predict long-term survival with discrete markers. Biometrical Journal 53.2 (2011): 294-307.

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

Please suggest features or report bugs with the GitHub issue tracker.

All documentation is copyright its authors; we didn't write any of that.