BOSO.single: BOSO.single and associates functions

Description

Bonjour

Usage

BOSO.single(
  x,
  y,
  xval,
  yval,
  nlambda = 100,
  lambda.min.ratio = ifelse(nrow(x) < ncol(x), 0.01, 1e-04),
  lambda = NULL,
  intercept = TRUE,
  standardize = TRUE,
  dfmin = 0,
  dfmax = NULL,
  costErrorVal = 1,
  costErrorTrain = 0,
  costVars = 0,
  Threads = 0,
  timeLimit = 1e+75
)

Arguments

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.
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 cplex is allowed to use. Default is 0 (automatic).
timeLimit: CPLEX parameter, time limit per problem provided to CPLEX. Default is 1e75 (infinite time).

Author

Luis V. Valcarcel

Details

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