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
)A `BOSO` object which contains the following information:
estimated betas
trianing x set used in BOSO (input parameter)
trianing x set used in BOSO (input parameter)
validation x set used in BOSO (input parameter)
validation x set used in BOSO (input parameter)
nlambda used by `BOSO` (input parameter)
if `BOSO` has used intercept (input parameter)
if `BOSO` has used standardization (input parameter)
Mean value of each variable. 0 if data has not been standarized
Standard deviation value of each variable. 0 if data has not been standarized
Mean value of output variable. 0 if data has not been standarized
Maximum number of variables set to be used by `BOSO` (input parameter)
list with the results of the final problem for each K
error in training set in the final problem
error in Validation set in the final problem of used by
lambda selected in the final problem of
number of initial variables
number of events in the training set
number of events in the validation set
index of variables which were stored in each iteration by `BOSO` in the block strategy
Input matrix, of dimension 'n' x 'p'. This is the data from the training partition. Its recommended to be class "matrix".
Response variable for the training dataset. A matrix of one column or a vector, with 'n' elements.
Input matrix, of dimension 'n' x 'p'. This is the data from the validation partition. Its recommended to be class "matrix".
Response variable for the validation dataset. A matrix of one column or a vector, with 'n' elements.
information criterion to be used. Default is 'eBIC'.
information criterion to be used in the block strategy. Default is the same as IC, but eBIC uses BIC for the block strategy.
The number of lambda values. Default is 100.
The number of lambda values in the block strategy part. Default is 10.
Smallest value for lambda, as a fraction of lambda.max, the (data derived) entry value.
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.
Boolean variable to indicate if intercept should be added or not. Default is false.
Boolean variable to indicate if data should be scaled according to mean(x) mean(y) and sd(x) or not. Default is false.
Maximum number of variables to be included in the problem. The intercept is not included in this number. Default is min(p,n).
maximum number of variables in the block strategy.
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.
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.
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.
CPLEX parameter, number of cores that CPLEX is allowed to use. Default is 0 (automatic).
CPLEX parameter, time limit per problem provided to CPLEX. Default is 1e75 (infinite time).
print progress, different levels: 1) print simple progress. 2) print result of blocks. 3) print each k in blocks Default is FALSE.
set seed for random number generator for the block strategy. Default is system default.
warmstart for CPLEX or use a different problem for each k. Default is False.
is the ratio over one that the information criterion must increase to be STOP. Default is 1e-3.
array of pre-selected variables. WARNING: debug feature.
Luis V. Valcarcel
Compute the BOSO for use one block. This function calls cplexAPI to solve the optimization problem
#This first example is a basic
#example of how to execute BOSO
data("sim.xy", package = "BOSO")
if (requireNamespace('cplexAPI')){
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)
}
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