Single Index Model using MAVE and Optimizing Bandwidth. — SensIAT_sim_outcome_modeler

Single index model estimation using minimum average variance estimation (MAVE). A direction is estimated using MAVE, and then the bandwidth is selected by minimization of the cross-validated pseudo-integrated squared error. Optionally, the initial coefficients of the outcome model can be re-estimated by optimization on a spherical manifold. This option requires the ManifoldOptim package.

Usage

SensIAT_sim_outcome_modeler_mave(
  formula,
  data,
  kernel = "K2_Biweight",
  mave.method = "meanMAVE",
  id = ..id..,
  bw.selection = c("ise", "mse"),
  bw.method = c("optim", "grid", "optimize"),
  bw.range = c(0.01, 1.5),
  reestimate.coef = FALSE,
  ...
)

Arguments

formula: The outcome model formula
data: The data to fit the outcome model to. Should only include follow-up data, i.e. time > 0.
kernel: The kernel to use for the outcome model.
mave.method: The method to use for the MAVE estimation.
id: The patient identifier variable for the data.
bw.selection: The criteria for bandwidth selection, either 'ise' for Integrated Squared Error or 'mse' for Mean Squared Error.
bw.method: The method for bandwidth selection, either 'optim' for using optimization or 'grid' for grid search.
bw.range: A numeric vector of length 2 indicating the range of bandwidths to consider for selection as a multiple of the standard deviation of the single index predictor.
reestimate.coef: Logical indicating whether to re-estimate the coefficients of the outcome model after bandwidth selection.
...: Additional arguments to be passed to optim.

Value

Object of class SensIAT::Single-index-outcome-model which contains the outcome model portion.