Description Usage Arguments Details Value See Also Examples

View source: R/MultiLambdaCVfun.R

Optimizes a cross-validated score w.r.t. ridge penalties for multiple data blocks.

1 2 3 4 5 | ```
optLambdas(penaltiesinit = NULL, XXblocks, Y, X1 = NULL, pairing = NULL, folds,
intercept = ifelse(class(Y) == "Surv", FALSE, TRUE), frac1 = NULL, score = "loglik",
model = NULL, epsIWLS = 0.001, maxItrIWLS = 25, traceCV = TRUE, reltol = 1e-04,
optmethod = ifelse(length(penaltiesinit) == 1, "Brent", "Nelder-Mead"), maxItropt = 500,
save = FALSE, parallel = FALSE, fixedpen = NULL, fixedseed = TRUE)
``` |

`penaltiesinit` |
Numeric vector. Initial values for penaltyparameters. May be obtained from |

`XXblocks` |
List of |

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

`X1` |
Matrix. Dimension |

`pairing` |
Numerical vector of length 3 or |

`folds` |
List, containing the splits of the samples. Usually obtained by |

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

`frac1` |
Scalar. Prior fraction of cases. Only relevant for |

`score` |
Character. See Details. |

`model` |
Character. Any of |

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

`maxItrIWLS` |
Integer. Maximum number of iterations used in IWLS. |

`traceCV` |
Boolean. Should the output of the IWLS algorithm be traced? |

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

`optmethod` |
Character. Optimization method. Any of the methods |

`maxItropt` |
Integer. Maximum number of iterations for |

`save` |
Boolean. If TRUE appends the penalties and resulting CVscore to global variable |

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

`fixedpen` |
Integer vector or |

`fixedseed` |
Boolean. Should the initialization be fixed? For reproducibility. |

See `Scoring`

for details on `score`

.
We highly recommend to use smooth scoring functions, in particular `"loglik"`

.
For ranking-based criteria like `auc`

and `cindex`

we advise to use repeated CV (see `CVfolds`

) to avoid ending up in any of the many local optima.

List, with components:

`optres` |
Output of the optimizer |

`optpen` |
Vector with determined optimal penalties |

`allsc` |
Matrix with CV scores for all penalty parameter configurations used by the optimizer |

`optLambdasWrap`

for i) (recommended) optimization in two steps: first global, then local; and ii) sequential optimization
when some data types are preferred over others. `fastCV2`

for initialization of penalties. A full demo and data are available from:

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

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | ```
data(dataXXmirmeth)
resp <- dataXXmirmeth[[1]]
XXmirmeth <- dataXXmirmeth[[2]]
# Find initial lambdas: fast CV per data block separately.
cvperblock2 <- fastCV2(XXblocks=XXmirmeth,Y=resp,kfold=10,fixedfolds = TRUE)
lambdas <- cvperblock2$lambdas
# Create (repeated) CV-splits of the data.
leftout <- CVfolds(Y=resp,kfold=10,nrepeat=3,fixedfolds = TRUE)
# One-pass optimization
# Increase the number of iterations for optimal results
jointlambdas <- optLambdas(penaltiesinit=lambdas, XXblocks=XXmirmeth,Y=resp,
folds=leftout,score="loglik",save=T,maxItropt=5)
``` |

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