pdose_semi_solver: pdose_semi_solver

Description

The pseudo direct learning optimization function for personalized dose finding with dimension reduction.

Usage

pdose_semi_solver(
  B,
  X,
  R,
  A,
  a_dist,
  a_seq,
  lambda,
  bw,
  rho,
  eta,
  gamma,
  tau,
  epsilon,
  btol,
  ftol,
  gtol,
  maxitr,
  verbose,
  ncore
)

Value

The optimizer B for the esitmating equation.

Arguments

B: A matrix of the parameters B, the columns are subject to the orthogonality constraint
X: The covariate matrix
R: The perosnalzied medicine reward
A: observed dose levels
a_dist: A kernel distance matrix for the observed dose and girds of the dose levels
a_seq: A grid of dose levels
lambda: The penalty for the GCV for the kernel ridge regression
bw: A Kernel bandwidth, assuming each variable have unit variance
rho: (don't change) Parameter for control the linear approximation in line search
eta: (don't change) Factor for decreasing the step size in the backtracking line search
gamma: (don't change) Parameter for updating C by Zhang and Hager (2004)
tau: (don't change) Step size for updating
epsilon: (don't change) Parameter for approximating numerical gradient
btol: (don't change) The $B$ parameter tolerance level
ftol: (don't change) Estimation equation 2-norm tolerance level
gtol: (don't change) Gradient tolerance level
maxitr: Maximum number of iterations
verbose: Should information be displayed

References

Zhou, W., Zhu, R., & Zeng, D. (2021). A parsimonious personalized dose-finding model via dimension reduction. Biometrika, 108(3), 643-659. DOI: tools:::Rd_expr_doi("10.1093/biomet/asaa087")