fit_SensIAT_single_index_norm1coef_model: Single Index Model using MAVE and Optimizing Bandwidth.

In SensIAT: Sensitivity Analysis for Irregular Assessment Times

Single Index Model using MAVE and Optimizing Bandwidth.

Description

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

fit_SensIAT_single_index_norm1coef_model(
  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 = 0,
  ...
)

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	number of iterations to go through.
...	Additional arguments to be passed to optim.

Value

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

SensIAT documentation built on Sept. 9, 2025, 5:50 p.m.