oscar: Main OSCAR fitting function

Description

This function fits an OSCAR model object to the provided training data with the desired model family.

Usage

oscar(
  x,
  y,
  k,
  w,
  family = "cox",
  metric,
  solver = 1,
  verb = 1,
  print = 3,
  kmax,
  sanitize = TRUE,
  percentage = 1,
  in_selection = 1,
  storeX = TRUE,
  storeY = TRUE,
  control,
  ...
)

Value

Fitted oscar-object

Arguments

x: Data matrix 'x'
y: Response vector/two-column matrix 'y' (see: family); number of rows equal to nrow(x)
k: Integer (0/1) kit indicator matrix; number of columns equal to ncol(x), Default: Unit diagonal indicator matrix
w: Kit cost weight vector w of length nrow(k), Default: Equal cost for all variables
family: Model family, should be one of: 'cox', 'mse'/'gaussian', or 'logistic, Default: 'cox'
metric: Goodness metric, Default(s): Concordance index for Cox, MSE for Gaussian, and AUC for logistic regression
solver: Solver used in the optimization, should be 1/'DBDC' or 2/'LMBM', Default: 1.
verb: Level of verbosity in R, Default: 1
print: Level of verbosity in Fortran (may not be visible on all terminals); should be an integer between range, range, Default: 3
kmax: Maximum k step tested, by default all k are tested from k to maximum dimensionality, Default: ncol(x)
sanitize: Whether input column names should be cleaned of potentially problematic symbols, Default: TRUE
percentage: Percentage of possible starting points used within range [0,1], Default: 1
in_selection: Which starting point selection strategy is used (1, 2 or 3), Default: 1
storeX: If data matrix X should be saved in the model object; turning this off might would help with memory, Default: TRUE
storeY: If data response Y should be saved in the model object; turning this off might would help with memory, Default: TRUE
control: Tuning parameters for the optimizers, see function oscar.control(), Default: see ?oscar.control
...: Additional parameters

Details

OSCAR utilizes the L0-pseudonorm, also known as the best subset selection, and makes use of a DC-formulation of the discrete feature selection task into a continuous one. Then an appropriate optimization algorithm is utilized to find optima at different cardinalities (k). The S4 model objects 'oscar' can then be passed on to various down-stream functions, such as oscar.pareto, oscar.cv, and oscar.bs, along with their supporting visualization functions.

Examples

Run this code

if(interactive()){
  data(ex)
  fit <- oscar(x=ex_X, y=ex_Y, k=ex_K, w=ex_c, family='cox')
  fit
}