BOSO.multiple.coldstart {BOSO} | R Documentation |

Function to run a single block BOSO problem, generating for each K a different CPLEX object.

BOSO.multiple.coldstart( x, y, xval, yval, nlambda = 100, IC = "eBIC", n.IC = NULL, p.IC = NULL, lambda.min.ratio = ifelse(nrow(x) < ncol(x), 0.01, 1e-04), lambda = NULL, intercept = TRUE, standardize = FALSE, dfmin = 0, dfmax = NULL, costErrorVal = 1, costErrorTrain = 0, costVars = 0, Threads = 0, timeLimit = 1e+75, verbose = F, TH_IC = 0.001 )

`x` |
Input matrix, of dimension 'n' x 'p'. This is the data from the training partition. Its recommended to be class "matrix". |

`y` |
Response variable for the training dataset. A matrix of one column or a vector, with 'n' elements |

`xval` |
Input matrix, of dimension 'n' x 'p'. This is the data from the validation partition. Its recommended to be class "matrix". |

`yval` |
Response variable for the validation dataset. A matrix of one column or a vector, with 'n' elements. |

`nlambda` |
The number of lambda values. Default is 100. |

`IC` |
information criterion to be used. Default is 'eBIC'.#' |

`n.IC` |
number of events for the information criterion. |

`p.IC` |
number of initial variables for the information criterion. |

`lambda.min.ratio` |
Smallest value for lambda, as a fraction of lambda.max, the (data derived) entry value. |

`lambda` |
A user supplied lambda sequence. Typical usage is to have the program compute its own lambda sequence based on nlambda and lambda.min.ratio. Supplying a value of lambda overrides this. WARNING: use with care |

`intercept` |
Boolean variable to indicate if intercept should be added or not. Default is false. |

`standardize` |
Boolean variable to indicate if data should be scaled according to mean(x) mean(y) and sd(x) or not. Default is false. |

`dfmin` |
Minimum number of variables to be included in the problem. The intercept is not included in this number. Default is 0. |

`dfmax` |
Maximum number of variables to be included in the problem. The intercept is not included in this number. Default is min(p,n). |

`costErrorVal` |
Cost of error of the validation set in the objective function. Default is 1. WARNING: use with care, changing this value changes the formulation presented in the main article. |

`costErrorTrain` |
Cost of error of the training set in the objective function. Default is 0. WARNING: use with care, changing this value changes the formulation presented in the main article. |

`costVars` |
Cost of new variables in the objective function. Default is 0. WARNING: use with care, changing this value changes the formulation presented in the main article. |

`Threads` |
CPLEX parameter, number of cores that IBM ILOG CPLEX is allowed to use. Default is 0 (automatic). |

`timeLimit` |
CPLEX parameter, time limit per problem provided to CPLEX. Default is 1e75 (infinite time). |

`verbose` |
print progress. Default is FALSE. |

`TH_IC` |
is the ratio over one that the information criterion must increase to be STOP. Default is 1e-3. |

Compute the BOSO for use one block. This function calls ILOG IBM CPLEX with 'cplexAPI' to solve the optimization problem

A 'BOSO' object.

Luis V. Valcarcel

[Package *BOSO* version 1.0.3 Index]