pdose_semi_solver: pdose_semi_solver

In orthoDr: Semi-Parametric Dimension Reduction Models Using Orthogonality Constrained Optimization

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
)

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

Value

The optimizer B for the esitmating equation.

References

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

orthoDr documentation built on April 30, 2023, 5:12 p.m.