GradRobustStep: Hill-Climbing algorithm to identify optimal GLMM design

In samuel-watson/glmmr: Design and analysis for generalised linear mixed models in R

Hill-Climbing algorithm to identify optimal GLMM design

Description

Hill-Climbing algorithm to identify optimal GLMM design

Usage

GradRobustStep(
  idx_in,
  n,
  C_list,
  X_list,
  Z_list,
  D_list,
  w_diag,
  max_obs,
  weights,
  exp_cond,
  nfix,
  V0_list,
  any_fix = 0L,
  type = 0L,
  rd_mode = 1L,
  trace = TRUE,
  uncorr = FALSE,
  bayes = FALSE
)

Arguments

idx_in	Integer vector specifying the indexes of the experimental conditions to start from
n	Integer specifying the size of the design to find. For local search, this should be equal to the size of idx_in
C_list	List of C vectors for the c-optimal function, see glmmr[DesignSpace]
X_list	List of X matrices
weights	Vector specifying the weights of each design
exp_cond	Vector specifying the experimental condition index of each observation
nfix	Vector listing the experimental condition indexes that are fixed in the design
any_fix	Integer. 0 = no experimental conditions are fixed, 1 = some experimental conditions are fixed
type	Integer. 0 = local search algorith. 1 = greedy search algorithm.
rd_mode	Integer. Robust objective function, 1=weighted average, 2=minimax
trace	Logical indicating whether to provide detailed output
N	Integer specifying number of experimental conditions in the optimal design
sig_list	List of inverse covariance matrices

Value

A vector of experimental condition indexes in the optimal design

samuel-watson/glmmr documentation built on July 27, 2022, 10:30 p.m.