EW_liftoneDoptimal_GLM_func: EW Lift-one algorithm for D-optimal approximate design

In ForLion: 'ForLion' Algorithm to Find D-Optimal Designs for Experiments

EW Lift-one algorithm for D-optimal approximate design

Description

EW Lift-one algorithm for D-optimal approximate design

Usage

EW_liftoneDoptimal_GLM_func(
  X,
  E_w,
  reltol = 1e-05,
  maxit = 100,
  random = FALSE,
  nram = 3,
  p00 = NULL
)

Arguments

X	Model matrix, with nrow = num of design points and ncol = num of parameters
E_w	Diagonal of E_W matrix in Fisher information matrix, can be calculated EW_Xw_maineffects_self() function in the ForLion package
reltol	reltol The relative convergence tolerance, default value 1e-5
maxit	The maximum number of iterations, default value 100
random	TRUE or FALSE, if TRUE then the function will run with additional "nram" number of initial allocation p00, default to be TRUE
nram	When random == TRUE, the function will generate nram number of initial points, default is 3
p00	Specified initial design approximate allocation; default to be NULL, this will generate a random initial design

Value

p EW D-optimal approximate allocation

p0 Initial approximate allocation that derived the reported EW D-optimal approximate allocation

Maximum The maximum of the determinant of the Fisher information matrix of the reported EW D-optimal design

convergence Convergence TRUE or FALSE

itmax number of the iteration

Examples

hfunc.temp = function(y) {c(y,1);};   # y -> h(y)=(y1,y2,y3,1)
link.temp="logit"
paras_lowerbound<-rep(-Inf, 4)
paras_upperbound<-rep(Inf, 4)
gjoint_b<- function(x) {
mu1 <- -0.5; sigma1 <- 1
mu2 <- 0.5; sigma2 <- 1
mu3 <- 1; sigma3 <- 1
mu0 <- 1; sigma0 <- 1
d1 <- stats::dnorm(x[1], mean = mu1, sd = sigma1)
d2 <- stats::dnorm(x[2], mean = mu2, sd = sigma2)
d3 <- stats::dnorm(x[3], mean = mu3, sd = sigma3)
d4 <- stats::dnorm(x[4], mean = mu0, sd = sigma0)
return(d1 * d2 * d3 * d4)
}
x.temp=matrix(data=c(-2,-1,-3,2,-1,-3,-2,1,-3,2,1,-3,-2,-1,3,2,-1,3,-2,1,3,2,1,3),ncol=3,byrow=TRUE)
m.temp=dim(x.temp)[1]     # number of design points
p.temp=length(paras_upperbound)    # number of predictors
Xmat.temp=matrix(0, m.temp, p.temp)
EW_wvec.temp=rep(0, m.temp)
for(i in 1:m.temp) {
htemp=EW_Xw_maineffects_self(x=x.temp[i,],joint_Func_b=gjoint_b, Lowerbounds=paras_lowerbound,
                             Upperbounds=paras_upperbound, link=link.temp, h.func=hfunc.temp);
Xmat.temp[i,]=htemp$X;
EW_wvec.temp[i]=htemp$E_w;
}
EW_liftoneDoptimal_GLM_func(X=Xmat.temp, E_w=EW_wvec.temp, reltol=1e-8, maxit=1000,
                            random=TRUE, nram=3, p00=NULL)

ForLion documentation built on April 11, 2025, 5:38 p.m.