Description Usage Arguments Details Value See Also Examples

View source: R/MultiLambdaCVfun.R

Fast cross-validation for high-dimensional data. Finds optimal penalties separately per data block. Useful for initialization.

1 2 3 |

`XXblocks` |
List of data frames or matrices, representing |

`Y` |
Response vector: numeric, binary, factor or |

`X1` |
Matrix. Dimension |

`kfold` |
Integer. Desired fold. |

`intercept` |
Boolean. Should an intercept be included? |

`parallel` |
Boolean. Should computation be done in parallel? If |

`fixedfolds` |
Boolean. Should fixed splits be used for reproducibility? |

`model` |
Character. Any of |

`eps` |
Scalar. Numerical bound for IWLS convergence. |

`reltol` |
Scalar. Relative tolerance for optimization method. |

`lambdamax` |
Numeric. Upperbound for lambda. |

`traceCV` |
Boolean. Should the CV results be traced and printed? |

This function is basically a wrapper for applying `optLambdas`

per data block separately using Brent optimization.

Numerical vector containing penalties optimized separately per data block. Useful for initialization.

`optLambdas`

, `optLambdasWrap`

which optimize the penalties jointly.
A full demo and data are available from:

https://drive.google.com/open?id=1NUfeOtN8-KZ8A2HZzveG506nBwgW64e4

1 2 3 4 5 6 | ```
data(dataXXmirmeth)
resp <- dataXXmirmeth[[1]]
XXmirmeth <- dataXXmirmeth[[2]]
cvperblock2 <- fastCV2(XXblocks=XXmirmeth,Y=resp,kfold=10,fixedfolds = TRUE)
lambdas <- cvperblock2$lambdas
``` |

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