BOSO {BOSO} | R Documentation |

Fit a ridge linear regression by a feature selection model conducted by BOSO MILP. The package 'cplexAPI' is necessary to run it.

BOSO( x, y, xval, yval, IC = "eBIC", IC.blocks = NULL, nlambda = 100, nlambda.blocks = 10, lambda.min.ratio = ifelse(nrow(x) < ncol(x), 0.01, 1e-04), lambda = NULL, intercept = TRUE, standardize = TRUE, dfmax = NULL, maxVarsBlock = 10, costErrorVal = 1, costErrorTrain = 0, costVars = 0, Threads = 0, timeLimit = 1e+75, verbose = F, seed = NULL, warmstart = F, TH_IC = 0.001, indexSelected = NULL )

`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. |

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

`IC.blocks` |
information criterion to be used in the block strategy. Default is the same as IC, but eBIC uses BIC for the block strategy. |

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

`nlambda.blocks` |
The number of lambda values in the block strategy part. Default is 10. |

`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. |

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

`maxVarsBlock` |
maximum number of variables in the block strategy. |

`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 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, different levels: 1) print simple progress. 2) print result of blocks. 3) print each k in blocks Default is FALSE. |

`seed` |
set seed for random number generator for the block strategy. Default is system default. |

`warmstart` |
warmstart for CPLEX or use a different problem for each k. Default is False. |

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

`indexSelected` |
array of pre-selected variables. WARNING: debug feature. |

Compute the BOSO for use one block. This function calls cplexAPI to solve the optimization problem

A 'BOSO' object which contains the following information:

`betas` |
estimated betas |

`x` |
trianing x set used in BOSO (input parameter) |

`y` |
trianing x set used in BOSO (input parameter) |

`xval` |
validation x set used in BOSO (input parameter) |

`yval` |
validation x set used in BOSO (input parameter) |

`nlambda` |
nlambda used by 'BOSO' (input parameter) |

`intercept` |
if 'BOSO' has used intercept (input parameter) |

`standardize` |
if 'BOSO' has used standardization (input parameter) |

`mx` |
Mean value of each variable. 0 if data has not been standarized |

`sx` |
Standard deviation value of each variable. 0 if data has not been standarized |

`my` |
Mean value of output variable. 0 if data has not been standarized |

`dfmax` |
Maximum number of variables set to be used by 'BOSO' (input parameter) |

`result.final` |
list with the results of the final problem for each K |

`errorTrain` |
error in training set in the final problem |

`errorVal` |
error in Validation set in the final problem of used by |

`lambda.selected` |
lambda selected in the final problem of |

`p` |
number of initial variables |

`n` |
number of events in the training set |

`nval` |
number of events in the validation set |

`blockStrategy` |
index of variables which were stored in each iteration by 'BOSO' in the block strategy |

Luis V. Valcarcel

#This first example is a basic #example of how to execute BOSO data("sim.xy", package = "BOSO") obj <- BOSO(x = sim.xy[['low']]$x, y = sim.xy[['low']]$y, xval = sim.xy[['low']]$xval, yval = sim.xy[['low']]$yval, IC = 'eBIC', nlambda=50, intercept= 0, standardize = 0, Threads=1, verbose = 3, seed = 2021) coef(obj) # extract coefficients at a single value of lambda predict(obj, newx = sim.xy[['low']]$x[1:20, ]) # make predictions

[Package *BOSO* version 1.0.3 Index]